diff options
| author | jarrodmillman <jarrod.millman@gmail.com> | 2022-12-15 16:11:13 +0000 |
|---|---|---|
| committer | jarrodmillman <jarrod.millman@gmail.com> | 2022-12-15 16:11:13 +0000 |
| commit | 2a8e7829bc111282c4e0a5ca091d8a27b6f81bed (patch) | |
| tree | 0e0ee6e918dfc085a5e8e3d8574c97091577efa8 | |
| parent | 832c558e3507e5cb667a622b5372f91384ab026f (diff) | |
| download | networkx-2a8e7829bc111282c4e0a5ca091d8a27b6f81bed.tar.gz | |
Deploying to gh-pages from @ networkx/networkx@740e981a29385cafd6df3cd6c3d612f9e9e54b3d 🚀
187 files changed, 677 insertions, 678 deletions
diff --git a/_downloads/01e2dc46cd3610f7ba9a53f9cfb580b2/plot_tsp.ipynb b/_downloads/01e2dc46cd3610f7ba9a53f9cfb580b2/plot_tsp.ipynb index 33155576..176c49e2 100644 --- a/_downloads/01e2dc46cd3610f7ba9a53f9cfb580b2/plot_tsp.ipynb +++ b/_downloads/01e2dc46cd3610f7ba9a53f9cfb580b2/plot_tsp.ipynb @@ -46,7 +46,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/_downloads/02562ba26c63bbc65b1ba9a876139e76/plot_weighted_graph.ipynb b/_downloads/02562ba26c63bbc65b1ba9a876139e76/plot_weighted_graph.ipynb index 65d9396d..1f4d1a0e 100644 --- a/_downloads/02562ba26c63bbc65b1ba9a876139e76/plot_weighted_graph.ipynb +++ b/_downloads/02562ba26c63bbc65b1ba9a876139e76/plot_weighted_graph.ipynb @@ -46,7 +46,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/_downloads/069762c4682cfa2ab2b72c8f020829b9/plot_napoleon_russian_campaign.ipynb b/_downloads/069762c4682cfa2ab2b72c8f020829b9/plot_napoleon_russian_campaign.ipynb index 9c7ea062..eb04523d 100644 --- a/_downloads/069762c4682cfa2ab2b72c8f020829b9/plot_napoleon_russian_campaign.ipynb +++ b/_downloads/069762c4682cfa2ab2b72c8f020829b9/plot_napoleon_russian_campaign.ipynb @@ -46,7 +46,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip b/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip Binary files differindex c99ba697..a50e396d 100644 --- a/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip +++ b/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip diff --git a/_downloads/097285ee56b831e03a1cf029dd432ed7/plot_multipartite_graph.ipynb b/_downloads/097285ee56b831e03a1cf029dd432ed7/plot_multipartite_graph.ipynb index 812bac6c..4d05ce14 100644 --- a/_downloads/097285ee56b831e03a1cf029dd432ed7/plot_multipartite_graph.ipynb +++ b/_downloads/097285ee56b831e03a1cf029dd432ed7/plot_multipartite_graph.ipynb @@ -46,7 +46,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/_downloads/13a207a5939541ecfa4ab2ab65ffd8f6/plot_dag_layout.ipynb b/_downloads/13a207a5939541ecfa4ab2ab65ffd8f6/plot_dag_layout.ipynb index b690a261..2a853daa 100644 --- a/_downloads/13a207a5939541ecfa4ab2ab65ffd8f6/plot_dag_layout.ipynb +++ b/_downloads/13a207a5939541ecfa4ab2ab65ffd8f6/plot_dag_layout.ipynb @@ -46,7 +46,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/_downloads/1824a5213ee1c1521c974d50ba43d761/plot_roget.ipynb b/_downloads/1824a5213ee1c1521c974d50ba43d761/plot_roget.ipynb index d64a1142..8c1cbd02 100644 --- a/_downloads/1824a5213ee1c1521c974d50ba43d761/plot_roget.ipynb +++ b/_downloads/1824a5213ee1c1521c974d50ba43d761/plot_roget.ipynb @@ -46,7 +46,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/_downloads/1ec7b0dc32695fec6a38afb4f6eb4b16/plot_dedensification.ipynb b/_downloads/1ec7b0dc32695fec6a38afb4f6eb4b16/plot_dedensification.ipynb index 0bd9962a..c6b63826 100644 --- a/_downloads/1ec7b0dc32695fec6a38afb4f6eb4b16/plot_dedensification.ipynb +++ b/_downloads/1ec7b0dc32695fec6a38afb4f6eb4b16/plot_dedensification.ipynb @@ -46,7 +46,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/_downloads/23bd8a075fec100ee75fd165948aad05/plot_chess_masters.ipynb b/_downloads/23bd8a075fec100ee75fd165948aad05/plot_chess_masters.ipynb index da78ad9f..13bd1f7c 100644 --- a/_downloads/23bd8a075fec100ee75fd165948aad05/plot_chess_masters.ipynb +++ b/_downloads/23bd8a075fec100ee75fd165948aad05/plot_chess_masters.ipynb @@ -46,7 +46,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/_downloads/2942c802f8354d6eda40a219b72f5d54/plot_erdos_renyi.ipynb b/_downloads/2942c802f8354d6eda40a219b72f5d54/plot_erdos_renyi.ipynb index 5a40a67d..d0bdb544 100644 --- a/_downloads/2942c802f8354d6eda40a219b72f5d54/plot_erdos_renyi.ipynb +++ b/_downloads/2942c802f8354d6eda40a219b72f5d54/plot_erdos_renyi.ipynb @@ -46,7 +46,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/_downloads/2a66ab839d29e0e6b9e6c0db64e50f74/plot_triad_types.ipynb b/_downloads/2a66ab839d29e0e6b9e6c0db64e50f74/plot_triad_types.ipynb index 65ed1e58..10936eb3 100644 --- a/_downloads/2a66ab839d29e0e6b9e6c0db64e50f74/plot_triad_types.ipynb +++ b/_downloads/2a66ab839d29e0e6b9e6c0db64e50f74/plot_triad_types.ipynb @@ -46,7 +46,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/_downloads/33ab82d52e7e2ba4702ffaa767978d75/plot_sampson.ipynb b/_downloads/33ab82d52e7e2ba4702ffaa767978d75/plot_sampson.ipynb index 6dc28135..5f06f78a 100644 --- a/_downloads/33ab82d52e7e2ba4702ffaa767978d75/plot_sampson.ipynb +++ b/_downloads/33ab82d52e7e2ba4702ffaa767978d75/plot_sampson.ipynb @@ -46,7 +46,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/_downloads/34585673e85649a6fc128bb278c2484e/plot_simple_graph.ipynb b/_downloads/34585673e85649a6fc128bb278c2484e/plot_simple_graph.ipynb index 4b7fb221..12dccadf 100644 --- a/_downloads/34585673e85649a6fc128bb278c2484e/plot_simple_graph.ipynb +++ b/_downloads/34585673e85649a6fc128bb278c2484e/plot_simple_graph.ipynb @@ -64,7 +64,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/_downloads/34e31faee4dff4e7c09914677852017c/plot_iterated_dynamical_systems.ipynb b/_downloads/34e31faee4dff4e7c09914677852017c/plot_iterated_dynamical_systems.ipynb index b2578b88..f00af769 100644 --- a/_downloads/34e31faee4dff4e7c09914677852017c/plot_iterated_dynamical_systems.ipynb +++ b/_downloads/34e31faee4dff4e7c09914677852017c/plot_iterated_dynamical_systems.ipynb @@ -46,7 +46,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/_downloads/35ba6dddd6b6024f6b53a3762807ada7/mayavi2_spring.ipynb b/_downloads/35ba6dddd6b6024f6b53a3762807ada7/mayavi2_spring.ipynb index 3406ffcb..659631dd 100644 --- a/_downloads/35ba6dddd6b6024f6b53a3762807ada7/mayavi2_spring.ipynb +++ b/_downloads/35ba6dddd6b6024f6b53a3762807ada7/mayavi2_spring.ipynb @@ -46,7 +46,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/_downloads/3733084a8d2fbc710d852d4aa6c8515c/plot_house_with_colors.ipynb b/_downloads/3733084a8d2fbc710d852d4aa6c8515c/plot_house_with_colors.ipynb index e6a37069..eb60eb8f 100644 --- a/_downloads/3733084a8d2fbc710d852d4aa6c8515c/plot_house_with_colors.ipynb +++ b/_downloads/3733084a8d2fbc710d852d4aa6c8515c/plot_house_with_colors.ipynb @@ -46,7 +46,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/_downloads/3a5de2178f4d4bb1892ea7036709f1bb/plot_morse_trie.ipynb b/_downloads/3a5de2178f4d4bb1892ea7036709f1bb/plot_morse_trie.ipynb index 7a25d080..944198b3 100644 --- a/_downloads/3a5de2178f4d4bb1892ea7036709f1bb/plot_morse_trie.ipynb +++ b/_downloads/3a5de2178f4d4bb1892ea7036709f1bb/plot_morse_trie.ipynb @@ -46,7 +46,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/_downloads/3adca49f1b673454c4138bf3eea6d2c5/plot_blockmodel.ipynb b/_downloads/3adca49f1b673454c4138bf3eea6d2c5/plot_blockmodel.ipynb index 897393ad..ccc5304d 100644 --- a/_downloads/3adca49f1b673454c4138bf3eea6d2c5/plot_blockmodel.ipynb +++ b/_downloads/3adca49f1b673454c4138bf3eea6d2c5/plot_blockmodel.ipynb @@ -46,7 +46,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/_downloads/3cf87d969750fa19e98e8534037c29c2/plot_points.ipynb b/_downloads/3cf87d969750fa19e98e8534037c29c2/plot_points.ipynb index db99bfa1..362ceebb 100644 --- a/_downloads/3cf87d969750fa19e98e8534037c29c2/plot_points.ipynb +++ b/_downloads/3cf87d969750fa19e98e8534037c29c2/plot_points.ipynb @@ -46,7 +46,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/_downloads/45bfb5158142c93e52e02b743963cad5/plot_rcm.ipynb b/_downloads/45bfb5158142c93e52e02b743963cad5/plot_rcm.ipynb index 774f06db..e2091814 100644 --- a/_downloads/45bfb5158142c93e52e02b743963cad5/plot_rcm.ipynb +++ b/_downloads/45bfb5158142c93e52e02b743963cad5/plot_rcm.ipynb @@ -46,7 +46,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/_downloads/461dfe5db4853b4fe245b7ec677c3f2e/plot_words.ipynb b/_downloads/461dfe5db4853b4fe245b7ec677c3f2e/plot_words.ipynb index 33e9d899..afd359ae 100644 --- a/_downloads/461dfe5db4853b4fe245b7ec677c3f2e/plot_words.ipynb +++ b/_downloads/461dfe5db4853b4fe245b7ec677c3f2e/plot_words.ipynb @@ -46,7 +46,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/_downloads/4aba8bc455260708046bf8ed7dcf62c6/plot_unix_email.ipynb b/_downloads/4aba8bc455260708046bf8ed7dcf62c6/plot_unix_email.ipynb index c5c504a4..c065eb9f 100644 --- a/_downloads/4aba8bc455260708046bf8ed7dcf62c6/plot_unix_email.ipynb +++ b/_downloads/4aba8bc455260708046bf8ed7dcf62c6/plot_unix_email.ipynb @@ -46,7 +46,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/_downloads/503721d5d744d7f74012c4d57cf76be9/plot_eigenvalues.ipynb b/_downloads/503721d5d744d7f74012c4d57cf76be9/plot_eigenvalues.ipynb index 4875b4c4..a604304a 100644 --- a/_downloads/503721d5d744d7f74012c4d57cf76be9/plot_eigenvalues.ipynb +++ b/_downloads/503721d5d744d7f74012c4d57cf76be9/plot_eigenvalues.ipynb @@ -46,7 +46,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/_downloads/538f38325dbb659c6ead9fd27aaef906/plot_subgraphs.ipynb b/_downloads/538f38325dbb659c6ead9fd27aaef906/plot_subgraphs.ipynb index 596c17fb..38e50064 100644 --- a/_downloads/538f38325dbb659c6ead9fd27aaef906/plot_subgraphs.ipynb +++ b/_downloads/538f38325dbb659c6ead9fd27aaef906/plot_subgraphs.ipynb @@ -172,7 +172,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/_downloads/5484f5e2a082adfed9b995018fd2a5c4/plot_lines.ipynb b/_downloads/5484f5e2a082adfed9b995018fd2a5c4/plot_lines.ipynb index 14c7fe5e..b716f541 100644 --- a/_downloads/5484f5e2a082adfed9b995018fd2a5c4/plot_lines.ipynb +++ b/_downloads/5484f5e2a082adfed9b995018fd2a5c4/plot_lines.ipynb @@ -154,7 +154,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/_downloads/57c2b737c0f26a2791d92b46da330676/plot_expected_degree_sequence.ipynb b/_downloads/57c2b737c0f26a2791d92b46da330676/plot_expected_degree_sequence.ipynb index 843f5c4a..ada644fa 100644 --- a/_downloads/57c2b737c0f26a2791d92b46da330676/plot_expected_degree_sequence.ipynb +++ b/_downloads/57c2b737c0f26a2791d92b46da330676/plot_expected_degree_sequence.ipynb @@ -46,7 +46,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/_downloads/58c0f28e678a58a40bb5ef3cbd845d87/plot_attributes.ipynb b/_downloads/58c0f28e678a58a40bb5ef3cbd845d87/plot_attributes.ipynb index 76bd0963..721c77ba 100644 --- a/_downloads/58c0f28e678a58a40bb5ef3cbd845d87/plot_attributes.ipynb +++ b/_downloads/58c0f28e678a58a40bb5ef3cbd845d87/plot_attributes.ipynb @@ -46,7 +46,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/_downloads/6395faf4e76e90dbf38b680165ad2a24/plot_rainbow_coloring.ipynb b/_downloads/6395faf4e76e90dbf38b680165ad2a24/plot_rainbow_coloring.ipynb index 2f4318b8..ecfd99f7 100644 --- a/_downloads/6395faf4e76e90dbf38b680165ad2a24/plot_rainbow_coloring.ipynb +++ b/_downloads/6395faf4e76e90dbf38b680165ad2a24/plot_rainbow_coloring.ipynb @@ -46,7 +46,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/_downloads/6744a0a3e3e1c506f1b51abc836d0993/plot_labels_and_colors.ipynb b/_downloads/6744a0a3e3e1c506f1b51abc836d0993/plot_labels_and_colors.ipynb index 8ae94af1..72767635 100644 --- a/_downloads/6744a0a3e3e1c506f1b51abc836d0993/plot_labels_and_colors.ipynb +++ b/_downloads/6744a0a3e3e1c506f1b51abc836d0993/plot_labels_and_colors.ipynb @@ -46,7 +46,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/_downloads/6811120d06ac32a67561df20c3909f9f/plot_simple_path.ipynb b/_downloads/6811120d06ac32a67561df20c3909f9f/plot_simple_path.ipynb index b70352ac..ea5ca49b 100644 --- a/_downloads/6811120d06ac32a67561df20c3909f9f/plot_simple_path.ipynb +++ b/_downloads/6811120d06ac32a67561df20c3909f9f/plot_simple_path.ipynb @@ -46,7 +46,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/_downloads/6d9caa91591d9d14d518144764a40afc/plot_random_geometric_graph.ipynb b/_downloads/6d9caa91591d9d14d518144764a40afc/plot_random_geometric_graph.ipynb index 0bedb845..eeb63752 100644 --- a/_downloads/6d9caa91591d9d14d518144764a40afc/plot_random_geometric_graph.ipynb +++ b/_downloads/6d9caa91591d9d14d518144764a40afc/plot_random_geometric_graph.ipynb @@ -46,7 +46,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/_downloads/6e2c510995a45587219c6f187a57f22b/plot_antigraph.ipynb b/_downloads/6e2c510995a45587219c6f187a57f22b/plot_antigraph.ipynb index ee9aa0f2..bc8e0d22 100644 --- a/_downloads/6e2c510995a45587219c6f187a57f22b/plot_antigraph.ipynb +++ b/_downloads/6e2c510995a45587219c6f187a57f22b/plot_antigraph.ipynb @@ -46,7 +46,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/_downloads/6e2f41828cdc6170bb3c4517097bdfc8/plot_snap.ipynb b/_downloads/6e2f41828cdc6170bb3c4517097bdfc8/plot_snap.ipynb index fcc62e45..acb22f24 100644 --- a/_downloads/6e2f41828cdc6170bb3c4517097bdfc8/plot_snap.ipynb +++ b/_downloads/6e2f41828cdc6170bb3c4517097bdfc8/plot_snap.ipynb @@ -46,7 +46,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip b/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip Binary files differindex 87b83ac5..4fb87f54 100644 --- a/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip +++ b/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip diff --git a/_downloads/73da379af64659b61cf9760ce786a7d4/plot_betweenness_centrality.ipynb b/_downloads/73da379af64659b61cf9760ce786a7d4/plot_betweenness_centrality.ipynb index 3b07770a..ed2bde3b 100644 --- a/_downloads/73da379af64659b61cf9760ce786a7d4/plot_betweenness_centrality.ipynb +++ b/_downloads/73da379af64659b61cf9760ce786a7d4/plot_betweenness_centrality.ipynb @@ -46,7 +46,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/_downloads/75057ffc857a59555c9505a02935679d/plot_degree_sequence.ipynb b/_downloads/75057ffc857a59555c9505a02935679d/plot_degree_sequence.ipynb index 959eda63..34925332 100644 --- a/_downloads/75057ffc857a59555c9505a02935679d/plot_degree_sequence.ipynb +++ b/_downloads/75057ffc857a59555c9505a02935679d/plot_degree_sequence.ipynb @@ -46,7 +46,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/_downloads/75f19de7ce4e4a3f6e69a89126fe0764/plot_degree.ipynb b/_downloads/75f19de7ce4e4a3f6e69a89126fe0764/plot_degree.ipynb index f38667e9..b5895e78 100644 --- a/_downloads/75f19de7ce4e4a3f6e69a89126fe0764/plot_degree.ipynb +++ b/_downloads/75f19de7ce4e4a3f6e69a89126fe0764/plot_degree.ipynb @@ -46,7 +46,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/_downloads/78c112310e3baf2494000ec899fe1d8c/plot_polygons.ipynb b/_downloads/78c112310e3baf2494000ec899fe1d8c/plot_polygons.ipynb index 7ba12d16..4317ed5b 100644 --- a/_downloads/78c112310e3baf2494000ec899fe1d8c/plot_polygons.ipynb +++ b/_downloads/78c112310e3baf2494000ec899fe1d8c/plot_polygons.ipynb @@ -46,7 +46,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/_downloads/795910d260da8b0e1d747500a0b724d6/plot_davis_club.ipynb b/_downloads/795910d260da8b0e1d747500a0b724d6/plot_davis_club.ipynb index a971b8a0..6bba3cca 100644 --- a/_downloads/795910d260da8b0e1d747500a0b724d6/plot_davis_club.ipynb +++ b/_downloads/795910d260da8b0e1d747500a0b724d6/plot_davis_club.ipynb @@ -46,7 +46,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/_downloads/82eb757c7a83f9d1a3bb8c5ae0bf9901/plot_four_grids.ipynb b/_downloads/82eb757c7a83f9d1a3bb8c5ae0bf9901/plot_four_grids.ipynb index 05ec1ec0..4dfe2acd 100644 --- a/_downloads/82eb757c7a83f9d1a3bb8c5ae0bf9901/plot_four_grids.ipynb +++ b/_downloads/82eb757c7a83f9d1a3bb8c5ae0bf9901/plot_four_grids.ipynb @@ -46,7 +46,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/_downloads/83685baeb4354793ec8ced7a7f1b7e59/plot_mini_atlas.ipynb b/_downloads/83685baeb4354793ec8ced7a7f1b7e59/plot_mini_atlas.ipynb index b9955b90..008cf9a6 100644 --- a/_downloads/83685baeb4354793ec8ced7a7f1b7e59/plot_mini_atlas.ipynb +++ b/_downloads/83685baeb4354793ec8ced7a7f1b7e59/plot_mini_atlas.ipynb @@ -46,7 +46,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/_downloads/916964531906bbec441fbea110913e0e/plot_node_colormap.ipynb b/_downloads/916964531906bbec441fbea110913e0e/plot_node_colormap.ipynb index fc3847ec..c3bb174b 100644 --- a/_downloads/916964531906bbec441fbea110913e0e/plot_node_colormap.ipynb +++ b/_downloads/916964531906bbec441fbea110913e0e/plot_node_colormap.ipynb @@ -46,7 +46,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/_downloads/92c38bebe8b49835778c05efc55350f7/plot_basic.ipynb b/_downloads/92c38bebe8b49835778c05efc55350f7/plot_basic.ipynb index d3296643..58e0c6d9 100644 --- a/_downloads/92c38bebe8b49835778c05efc55350f7/plot_basic.ipynb +++ b/_downloads/92c38bebe8b49835778c05efc55350f7/plot_basic.ipynb @@ -46,7 +46,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/_downloads/92d7c762b0638dc8f14acbf6549191b2/plot_center_node.ipynb b/_downloads/92d7c762b0638dc8f14acbf6549191b2/plot_center_node.ipynb index bff2e54e..33ae8efe 100644 --- a/_downloads/92d7c762b0638dc8f14acbf6549191b2/plot_center_node.ipynb +++ b/_downloads/92d7c762b0638dc8f14acbf6549191b2/plot_center_node.ipynb @@ -46,7 +46,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/_downloads/93c3636e507f6fdc185365de08b3cf95/plot_ego_graph.ipynb b/_downloads/93c3636e507f6fdc185365de08b3cf95/plot_ego_graph.ipynb index 0f6de05c..b31c5d6b 100644 --- a/_downloads/93c3636e507f6fdc185365de08b3cf95/plot_ego_graph.ipynb +++ b/_downloads/93c3636e507f6fdc185365de08b3cf95/plot_ego_graph.ipynb @@ -46,7 +46,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/_downloads/986a59ec868d5ccd5898a14041e59a9a/plot_delaunay.ipynb b/_downloads/986a59ec868d5ccd5898a14041e59a9a/plot_delaunay.ipynb index 10f30de4..7223a670 100644 --- a/_downloads/986a59ec868d5ccd5898a14041e59a9a/plot_delaunay.ipynb +++ b/_downloads/986a59ec868d5ccd5898a14041e59a9a/plot_delaunay.ipynb @@ -46,7 +46,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/_downloads/9dde3f21e79c59c2c3810ae2f2d709d1/plot_parallel_betweenness.ipynb b/_downloads/9dde3f21e79c59c2c3810ae2f2d709d1/plot_parallel_betweenness.ipynb index 8ea45177..85f1eedb 100644 --- a/_downloads/9dde3f21e79c59c2c3810ae2f2d709d1/plot_parallel_betweenness.ipynb +++ b/_downloads/9dde3f21e79c59c2c3810ae2f2d709d1/plot_parallel_betweenness.ipynb @@ -46,7 +46,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/_downloads/9e08abb01f5b05b033209e99cde912e7/plot_football.ipynb b/_downloads/9e08abb01f5b05b033209e99cde912e7/plot_football.ipynb index 854818b8..af90c2a2 100644 --- a/_downloads/9e08abb01f5b05b033209e99cde912e7/plot_football.ipynb +++ b/_downloads/9e08abb01f5b05b033209e99cde912e7/plot_football.ipynb @@ -46,7 +46,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/_downloads/9f6155a0dbed78cb471dc8404ca50d9a/plot_directed.ipynb b/_downloads/9f6155a0dbed78cb471dc8404ca50d9a/plot_directed.ipynb index 3c1c85e1..8657f373 100644 --- a/_downloads/9f6155a0dbed78cb471dc8404ca50d9a/plot_directed.ipynb +++ b/_downloads/9f6155a0dbed78cb471dc8404ca50d9a/plot_directed.ipynb @@ -46,7 +46,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/_downloads/a3b87ac2fc48acdb66126870fe56af8d/plot_custom_node_icons.ipynb b/_downloads/a3b87ac2fc48acdb66126870fe56af8d/plot_custom_node_icons.ipynb index f2571682..58ba7028 100644 --- a/_downloads/a3b87ac2fc48acdb66126870fe56af8d/plot_custom_node_icons.ipynb +++ b/_downloads/a3b87ac2fc48acdb66126870fe56af8d/plot_custom_node_icons.ipynb @@ -46,7 +46,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/_downloads/a579d34cb45334852a482c685362f008/plot_circular_tree.ipynb b/_downloads/a579d34cb45334852a482c685362f008/plot_circular_tree.ipynb index 8ec3757c..259ff624 100644 --- a/_downloads/a579d34cb45334852a482c685362f008/plot_circular_tree.ipynb +++ b/_downloads/a579d34cb45334852a482c685362f008/plot_circular_tree.ipynb @@ -46,7 +46,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/_downloads/aecdfc218fe5e5527193604e65e250da/plot_grid.ipynb b/_downloads/aecdfc218fe5e5527193604e65e250da/plot_grid.ipynb index b9c5d861..26069584 100644 --- a/_downloads/aecdfc218fe5e5527193604e65e250da/plot_grid.ipynb +++ b/_downloads/aecdfc218fe5e5527193604e65e250da/plot_grid.ipynb @@ -64,7 +64,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/_downloads/b082eca557d83b4887dafbe0f1534ede/plot_circuits.ipynb b/_downloads/b082eca557d83b4887dafbe0f1534ede/plot_circuits.ipynb index b0bc7d15..db44bedc 100644 --- a/_downloads/b082eca557d83b4887dafbe0f1534ede/plot_circuits.ipynb +++ b/_downloads/b082eca557d83b4887dafbe0f1534ede/plot_circuits.ipynb @@ -64,7 +64,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/_downloads/b3e9e8ac6c0b3bf8351bf0fb48976f9b/javascript_force.ipynb b/_downloads/b3e9e8ac6c0b3bf8351bf0fb48976f9b/javascript_force.ipynb index e24528cb..fa28b244 100644 --- a/_downloads/b3e9e8ac6c0b3bf8351bf0fb48976f9b/javascript_force.ipynb +++ b/_downloads/b3e9e8ac6c0b3bf8351bf0fb48976f9b/javascript_force.ipynb @@ -46,7 +46,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/_downloads/bb559e629cb03c663515d897916707b4/plot_printgraph.ipynb b/_downloads/bb559e629cb03c663515d897916707b4/plot_printgraph.ipynb index 770425c0..061d9af5 100644 --- a/_downloads/bb559e629cb03c663515d897916707b4/plot_printgraph.ipynb +++ b/_downloads/bb559e629cb03c663515d897916707b4/plot_printgraph.ipynb @@ -46,7 +46,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/_downloads/bc2ededffc7551b6c1c946248eac7db0/plot_selfloops.ipynb b/_downloads/bc2ededffc7551b6c1c946248eac7db0/plot_selfloops.ipynb index 2872ff97..af0b1e3e 100644 --- a/_downloads/bc2ededffc7551b6c1c946248eac7db0/plot_selfloops.ipynb +++ b/_downloads/bc2ededffc7551b6c1c946248eac7db0/plot_selfloops.ipynb @@ -46,7 +46,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/_downloads/c4496047a2c700bfca2f6e418e02ca48/plot_edge_colormap.ipynb b/_downloads/c4496047a2c700bfca2f6e418e02ca48/plot_edge_colormap.ipynb index b501369f..8632c0d1 100644 --- a/_downloads/c4496047a2c700bfca2f6e418e02ca48/plot_edge_colormap.ipynb +++ b/_downloads/c4496047a2c700bfca2f6e418e02ca48/plot_edge_colormap.ipynb @@ -46,7 +46,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/_downloads/d071301e92e895067d308e076bb57540/plot_knuth_miles.ipynb b/_downloads/d071301e92e895067d308e076bb57540/plot_knuth_miles.ipynb index 1ab41480..eeff6586 100644 --- a/_downloads/d071301e92e895067d308e076bb57540/plot_knuth_miles.ipynb +++ b/_downloads/d071301e92e895067d308e076bb57540/plot_knuth_miles.ipynb @@ -46,7 +46,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/_downloads/d39d36711a0bd1b3740f98e114ca90f8/plot_conversion.ipynb b/_downloads/d39d36711a0bd1b3740f98e114ca90f8/plot_conversion.ipynb index 954b7d6d..34f6618b 100644 --- a/_downloads/d39d36711a0bd1b3740f98e114ca90f8/plot_conversion.ipynb +++ b/_downloads/d39d36711a0bd1b3740f98e114ca90f8/plot_conversion.ipynb @@ -46,7 +46,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/_downloads/d45f3540b670180e2697f819757458cf/plot_karate_club.ipynb b/_downloads/d45f3540b670180e2697f819757458cf/plot_karate_club.ipynb index 2dc9d84f..37aa4a99 100644 --- a/_downloads/d45f3540b670180e2697f819757458cf/plot_karate_club.ipynb +++ b/_downloads/d45f3540b670180e2697f819757458cf/plot_karate_club.ipynb @@ -46,7 +46,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/_downloads/d4ff05da55438abd8747b5d94df09ea1/plot_beam_search.ipynb b/_downloads/d4ff05da55438abd8747b5d94df09ea1/plot_beam_search.ipynb index c1612ba3..988881d9 100644 --- a/_downloads/d4ff05da55438abd8747b5d94df09ea1/plot_beam_search.ipynb +++ b/_downloads/d4ff05da55438abd8747b5d94df09ea1/plot_beam_search.ipynb @@ -64,7 +64,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/_downloads/d798004efda2cd253f45c4a1a8013022/plot_giant_component.ipynb b/_downloads/d798004efda2cd253f45c4a1a8013022/plot_giant_component.ipynb index 09c5c9c6..be42e030 100644 --- a/_downloads/d798004efda2cd253f45c4a1a8013022/plot_giant_component.ipynb +++ b/_downloads/d798004efda2cd253f45c4a1a8013022/plot_giant_component.ipynb @@ -46,7 +46,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git 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<span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_blockmodel.py</span></code></a></p> diff --git a/auto_examples/algorithms/plot_circuits.html b/auto_examples/algorithms/plot_circuits.html index 410a2ed2..0c0bc9f7 100644 --- a/auto_examples/algorithms/plot_circuits.html +++ b/auto_examples/algorithms/plot_circuits.html @@ -603,7 +603,7 @@ fourth layer.</p> <img src="../../_images/sphx_glr_plot_circuits_001.png" srcset="../../_images/sphx_glr_plot_circuits_001.png" alt="((x ∨ y) ∧ (y ∨ ¬(z)))" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>((x ∨ y) ∧ (y ∨ ¬(z))) </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.106 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.100 seconds)</p> <div class="sphx-glr-footer 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class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.074 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.071 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-algorithms-plot-davis-club-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/6a1e333663010969e61d07b33c7845f0/plot_davis_club.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_davis_club.py</span></code></a></p> diff --git a/auto_examples/algorithms/plot_dedensification.html b/auto_examples/algorithms/plot_dedensification.html index aceb52e9..094464b3 100644 --- a/auto_examples/algorithms/plot_dedensification.html +++ b/auto_examples/algorithms/plot_dedensification.html @@ -593,7 +593,7 @@ would result in fewer edges in the compressed graph.</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.247 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.238 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-algorithms-plot-dedensification-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/868e28431bab2565b22bfbab847e1153/plot_dedensification.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_dedensification.py</span></code></a></p> diff --git a/auto_examples/algorithms/plot_iterated_dynamical_systems.html b/auto_examples/algorithms/plot_iterated_dynamical_systems.html index a7812d68..362a94f5 100644 --- a/auto_examples/algorithms/plot_iterated_dynamical_systems.html +++ b/auto_examples/algorithms/plot_iterated_dynamical_systems.html @@ -699,7 +699,7 @@ fixed points are [] <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"fixed points are </span><span class="si">{</span><span class="n">fixed_points</span><span class="p">(</span><span class="n">G</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.098 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.094 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-algorithms-plot-iterated-dynamical-systems-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/d947686c24b50c278c1228ff766cda27/plot_iterated_dynamical_systems.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_iterated_dynamical_systems.py</span></code></a></p> diff --git a/auto_examples/algorithms/plot_krackhardt_centrality.html b/auto_examples/algorithms/plot_krackhardt_centrality.html index 7c728c53..3e5c13cd 100644 --- a/auto_examples/algorithms/plot_krackhardt_centrality.html +++ b/auto_examples/algorithms/plot_krackhardt_centrality.html @@ -569,7 +569,7 @@ Closeness centrality <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.061 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.058 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-algorithms-plot-krackhardt-centrality-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/e77acafa90a347f4353549d3bffbb72c/plot_krackhardt_centrality.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_krackhardt_centrality.py</span></code></a></p> diff --git a/auto_examples/algorithms/plot_parallel_betweenness.html b/auto_examples/algorithms/plot_parallel_betweenness.html index 045c383c..623ac24c 100644 --- a/auto_examples/algorithms/plot_parallel_betweenness.html +++ b/auto_examples/algorithms/plot_parallel_betweenness.html @@ -517,29 +517,29 @@ faster. This is a limitation of our CI/CD pipeline running on a single core.</p> <img src="../../_images/sphx_glr_plot_parallel_betweenness_001.png" srcset="../../_images/sphx_glr_plot_parallel_betweenness_001.png" alt="plot parallel betweenness" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Computing betweenness centrality for: Graph with 1000 nodes and 2991 edges Parallel version - Time: 1.7620 seconds - Betweenness centrality for node 0: 0.18618 + Time: 1.7256 seconds + Betweenness centrality for node 0: 0.06802 Non-Parallel version - Time: 2.9848 seconds - Betweenness centrality for node 0: 0.18618 + Time: 2.7711 seconds + Betweenness centrality for node 0: 0.06802 Computing betweenness centrality for: -Graph with 1000 nodes and 5060 edges +Graph with 1000 nodes and 4870 edges Parallel version - Time: 2.2889 seconds - Betweenness centrality for node 0: 0.00052 + Time: 2.0861 seconds + Betweenness centrality for node 0: 0.00115 Non-Parallel version - Time: 3.9518 seconds - Betweenness centrality for node 0: 0.00052 + Time: 3.6317 seconds + Betweenness centrality for node 0: 0.00115 Computing betweenness centrality for: Graph with 1000 nodes and 2000 edges Parallel version - Time: 1.5487 seconds - Betweenness centrality for node 0: 0.00751 + Time: 1.4399 seconds + Betweenness centrality for node 0: 0.00189 Non-Parallel version - Time: 2.7100 seconds - Betweenness centrality for node 0: 0.00751 + Time: 2.5251 seconds + Betweenness centrality for node 0: 0.00189 </pre></div> </div> <div class="line-block"> @@ -611,7 +611,7 @@ Graph with 1000 nodes and 2000 edges <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 21.344 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 19.471 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-algorithms-plot-parallel-betweenness-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/8a9ce246f32a6cf6abd470292c7ffa6a/plot_parallel_betweenness.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_parallel_betweenness.py</span></code></a></p> diff --git a/auto_examples/algorithms/plot_rcm.html b/auto_examples/algorithms/plot_rcm.html index 080da09f..aa5670b8 100644 --- a/auto_examples/algorithms/plot_rcm.html +++ b/auto_examples/algorithms/plot_rcm.html @@ -615,7 +615,7 @@ bandwidth: 7 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.807 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.944 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-algorithms-plot-rcm-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/544d21367fbc1520a180d8891369bb49/plot_rcm.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_rcm.py</span></code></a></p> diff --git a/auto_examples/algorithms/plot_snap.html b/auto_examples/algorithms/plot_snap.html index f153eaa2..e71caaec 100644 --- a/auto_examples/algorithms/plot_snap.html +++ b/auto_examples/algorithms/plot_snap.html @@ -610,7 +610,7 @@ graph.</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.178 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.163 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-algorithms-plot-snap-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/0a756ab7ea4b899fa151e327a4dce8d2/plot_snap.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_snap.py</span></code></a></p> diff --git a/auto_examples/algorithms/plot_subgraphs.html b/auto_examples/algorithms/plot_subgraphs.html index 9759ab60..5653c78c 100644 --- a/auto_examples/algorithms/plot_subgraphs.html +++ b/auto_examples/algorithms/plot_subgraphs.html @@ -678,7 +678,7 @@ of subgraphs that contain only entirely <code class="xref py py-obj docutils lit <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<img src="../../_images/sphx_glr_plot_subgraphs_007.png" srcset="../../_images/sphx_glr_plot_subgraphs_007.png" alt="The reconstructed graph." class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.673 seconds)</p> +<img src="../../_images/sphx_glr_plot_subgraphs_007.png" srcset="../../_images/sphx_glr_plot_subgraphs_007.png" alt="The reconstructed graph." class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.640 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-algorithms-plot-subgraphs-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/7c14530887a80b15e4b4f3d68b23d114/plot_subgraphs.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_subgraphs.py</span></code></a></p> diff --git a/auto_examples/algorithms/sg_execution_times.html b/auto_examples/algorithms/sg_execution_times.html index 6a7e33f1..1bf6ba23 100644 --- a/auto_examples/algorithms/sg_execution_times.html +++ b/auto_examples/algorithms/sg_execution_times.html @@ -463,55 +463,55 @@ <section id="computation-times"> <span id="sphx-glr-auto-examples-algorithms-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this heading">#</a></h1> -<p><strong>00:28.057</strong> total execution time for <strong>auto_examples_algorithms</strong> files:</p> +<p><strong>00:25.750</strong> total execution time for <strong>auto_examples_algorithms</strong> files:</p> <table class="table"> <tbody> <tr class="row-odd"><td><p><a class="reference internal" href="plot_parallel_betweenness.html#sphx-glr-auto-examples-algorithms-plot-parallel-betweenness-py"><span class="std std-ref">Parallel Betweenness</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_parallel_betweenness.py</span></code>)</p></td> -<td><p>00:21.344</p></td> +<td><p>00:19.471</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_betweenness_centrality.html#sphx-glr-auto-examples-algorithms-plot-betweenness-centrality-py"><span class="std std-ref">Betweeness Centrality</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_betweenness_centrality.py</span></code>)</p></td> -<td><p>00:03.863</p></td> +<td><p>00:03.381</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_rcm.html#sphx-glr-auto-examples-algorithms-plot-rcm-py"><span class="std std-ref">Reverse Cuthill–McKee</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_rcm.py</span></code>)</p></td> -<td><p>00:00.807</p></td> +<td><p>00:00.944</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_subgraphs.html#sphx-glr-auto-examples-algorithms-plot-subgraphs-py"><span class="std std-ref">Subgraphs</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_subgraphs.py</span></code>)</p></td> -<td><p>00:00.673</p></td> +<td><p>00:00.640</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_blockmodel.html#sphx-glr-auto-examples-algorithms-plot-blockmodel-py"><span class="std std-ref">Blockmodel</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_blockmodel.py</span></code>)</p></td> -<td><p>00:00.392</p></td> +<td><p>00:00.380</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_dedensification.html#sphx-glr-auto-examples-algorithms-plot-dedensification-py"><span class="std std-ref">Dedensification</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_dedensification.py</span></code>)</p></td> -<td><p>00:00.247</p></td> +<td><p>00:00.238</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_beam_search.html#sphx-glr-auto-examples-algorithms-plot-beam-search-py"><span class="std std-ref">Beam Search</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_beam_search.py</span></code>)</p></td> -<td><p>00:00.213</p></td> +<td><p>00:00.210</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_snap.html#sphx-glr-auto-examples-algorithms-plot-snap-py"><span class="std std-ref">SNAP Graph Summary</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_snap.py</span></code>)</p></td> -<td><p>00:00.178</p></td> +<td><p>00:00.163</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_circuits.html#sphx-glr-auto-examples-algorithms-plot-circuits-py"><span class="std std-ref">Circuits</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_circuits.py</span></code>)</p></td> -<td><p>00:00.106</p></td> +<td><p>00:00.100</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_iterated_dynamical_systems.html#sphx-glr-auto-examples-algorithms-plot-iterated-dynamical-systems-py"><span class="std std-ref">Iterated Dynamical Systems</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_iterated_dynamical_systems.py</span></code>)</p></td> -<td><p>00:00.098</p></td> +<td><p>00:00.094</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_davis_club.html#sphx-glr-auto-examples-algorithms-plot-davis-club-py"><span class="std std-ref">Davis Club</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_davis_club.py</span></code>)</p></td> -<td><p>00:00.074</p></td> +<td><p>00:00.071</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_krackhardt_centrality.html#sphx-glr-auto-examples-algorithms-plot-krackhardt-centrality-py"><span class="std std-ref">Krackhardt Centrality</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_krackhardt_centrality.py</span></code>)</p></td> -<td><p>00:00.061</p></td> +<td><p>00:00.058</p></td> <td><p>0.0 MB</p></td> </tr> </tbody> diff --git a/auto_examples/basic/plot_properties.html b/auto_examples/basic/plot_properties.html index 2d8d36fd..47f4d1f0 100644 --- a/auto_examples/basic/plot_properties.html +++ b/auto_examples/basic/plot_properties.html @@ -574,7 +574,7 @@ density: 0.26666666666666666 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.090 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.084 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-basic-plot-properties-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/40632926e1e0842cea9103529e4bea12/plot_properties.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_properties.py</span></code></a></p> diff --git a/auto_examples/basic/plot_read_write.html b/auto_examples/basic/plot_read_write.html index 993cf4e1..f21f9c0c 100644 --- a/auto_examples/basic/plot_read_write.html +++ b/auto_examples/basic/plot_read_write.html @@ -545,7 +545,7 @@ to download the full example code</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.062 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.059 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-basic-plot-read-write-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/63b2264e53e5d28aeb43b6aa768515b9/plot_read_write.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_read_write.py</span></code></a></p> diff --git a/auto_examples/basic/plot_simple_graph.html b/auto_examples/basic/plot_simple_graph.html index b99a5e54..f17dd227 100644 --- a/auto_examples/basic/plot_simple_graph.html +++ b/auto_examples/basic/plot_simple_graph.html @@ -550,7 +550,7 @@ to download the full example code</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<img src="../../_images/sphx_glr_plot_simple_graph_002.png" srcset="../../_images/sphx_glr_plot_simple_graph_002.png" alt="plot simple graph" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.487 seconds)</p> +<img src="../../_images/sphx_glr_plot_simple_graph_002.png" srcset="../../_images/sphx_glr_plot_simple_graph_002.png" alt="plot simple graph" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.376 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-basic-plot-simple-graph-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/0f222beedce48fe624efff9ff2fdc89f/plot_simple_graph.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_simple_graph.py</span></code></a></p> diff --git a/auto_examples/basic/sg_execution_times.html b/auto_examples/basic/sg_execution_times.html index d75b747f..e95b4f5f 100644 --- a/auto_examples/basic/sg_execution_times.html +++ b/auto_examples/basic/sg_execution_times.html @@ -463,19 +463,19 @@ <section id="computation-times"> <span id="sphx-glr-auto-examples-basic-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this heading">#</a></h1> -<p><strong>00:00.639</strong> total execution time for <strong>auto_examples_basic</strong> files:</p> +<p><strong>00:00.519</strong> total execution time for <strong>auto_examples_basic</strong> files:</p> <table class="table"> <tbody> <tr class="row-odd"><td><p><a class="reference internal" href="plot_simple_graph.html#sphx-glr-auto-examples-basic-plot-simple-graph-py"><span class="std std-ref">Simple graph</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_simple_graph.py</span></code>)</p></td> -<td><p>00:00.487</p></td> +<td><p>00:00.376</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_properties.html#sphx-glr-auto-examples-basic-plot-properties-py"><span class="std std-ref">Properties</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_properties.py</span></code>)</p></td> -<td><p>00:00.090</p></td> +<td><p>00:00.084</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_read_write.html#sphx-glr-auto-examples-basic-plot-read-write-py"><span class="std std-ref">Read and write graphs.</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_read_write.py</span></code>)</p></td> -<td><p>00:00.062</p></td> +<td><p>00:00.059</p></td> <td><p>0.0 MB</p></td> </tr> </tbody> diff --git a/auto_examples/drawing/plot_center_node.html b/auto_examples/drawing/plot_center_node.html index 5b0723ae..70c4ac51 100644 --- a/auto_examples/drawing/plot_center_node.html +++ b/auto_examples/drawing/plot_center_node.html @@ -530,7 +530,7 @@ to download the full example code</p> <span class="n">nx</span><span class="o">.</span><span class="n">draw</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pos</span></a><span class="p">,</span> <span class="n">with_labels</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.068 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.063 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-center-node-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/8561539ed0b99621dbdbe53646ac5075/plot_center_node.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_center_node.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_chess_masters.html b/auto_examples/drawing/plot_chess_masters.html index 9ea5180d..a4df74c7 100644 --- a/auto_examples/drawing/plot_chess_masters.html +++ b/auto_examples/drawing/plot_chess_masters.html @@ -536,7 +536,7 @@ to black and contains selected game info.</p> <img src="../../_images/sphx_glr_plot_chess_masters_001.png" srcset="../../_images/sphx_glr_plot_chess_masters_001.png" alt="World Chess Championship Games: 1886 - 1985" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Loaded 685 chess games between 25 players Note the disconnected component consisting of: -['Karpov, Anatoly', 'Korchnoi, Viktor L', 'Kasparov, Gary'] +['Korchnoi, Viktor L', 'Karpov, Anatoly', 'Kasparov, Gary'] From a total of 237 different openings, the following games used the Sicilian opening @@ -702,7 +702,7 @@ findfont: Font family 'Helvetica' not found. <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.406 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.371 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-chess-masters-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/388158421a67216f605c1bbf9aa310bf/plot_chess_masters.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_chess_masters.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_custom_node_icons.html b/auto_examples/drawing/plot_custom_node_icons.html index 66927fc4..f932a216 100644 --- a/auto_examples/drawing/plot_custom_node_icons.html +++ b/auto_examples/drawing/plot_custom_node_icons.html @@ -585,7 +585,7 @@ to download the full example code</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.289 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.264 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-custom-node-icons-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/b580b9776494e714c1fb1880f03524a8/plot_custom_node_icons.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_custom_node_icons.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_degree.html b/auto_examples/drawing/plot_degree.html index aadc5a36..866a2860 100644 --- a/auto_examples/drawing/plot_degree.html +++ b/auto_examples/drawing/plot_degree.html @@ -561,7 +561,7 @@ each node is determined, and a figure is generated showing three things: <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.268 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.253 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-degree-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/70eaef0d99343cf8d3d6e70c803ad5a8/plot_degree.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_degree.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_directed.html b/auto_examples/drawing/plot_directed.html index dd6f7341..53af2914 100644 --- a/auto_examples/drawing/plot_directed.html +++ b/auto_examples/drawing/plot_directed.html @@ -556,7 +556,7 @@ to download the full example code</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.217 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.199 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-directed-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/6c2f9c3544cb695b31867eecc0f7fb1e/plot_directed.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_directed.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_edge_colormap.html b/auto_examples/drawing/plot_edge_colormap.html index c45b5fe7..d4e651c9 100644 --- a/auto_examples/drawing/plot_edge_colormap.html +++ b/auto_examples/drawing/plot_edge_colormap.html @@ -534,7 +534,7 @@ to download the full example code</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.064 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.060 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-edge-colormap-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/7ea4dc8cf44604668540ed81d6abebda/plot_edge_colormap.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_edge_colormap.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_ego_graph.html b/auto_examples/drawing/plot_ego_graph.html index 4413748c..fda8da3f 100644 --- a/auto_examples/drawing/plot_ego_graph.html +++ b/auto_examples/drawing/plot_ego_graph.html @@ -546,7 +546,7 @@ the largest hub in a Barabási-Albert network.</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.099 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.097 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-ego-graph-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/773fa56bdb128b8bd2a4f4a0e4dd38aa/plot_ego_graph.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_ego_graph.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_eigenvalues.html b/auto_examples/drawing/plot_eigenvalues.html index 8c2eae27..002470cb 100644 --- a/auto_examples/drawing/plot_eigenvalues.html +++ b/auto_examples/drawing/plot_eigenvalues.html @@ -541,7 +541,7 @@ Smallest eigenvalue: -5.239273865810698e-16 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.698 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.653 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-eigenvalues-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/a8660a7bb6b65b5a644025485c973cb9/plot_eigenvalues.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_eigenvalues.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_four_grids.html b/auto_examples/drawing/plot_four_grids.html index 706c25a9..1495873e 100644 --- a/auto_examples/drawing/plot_four_grids.html +++ b/auto_examples/drawing/plot_four_grids.html @@ -562,7 +562,7 @@ customize the visualization of a simple Graph comprising a 4x4 grid.</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.333 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.301 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-four-grids-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/4136c066ab1d073cf527e9dc02bfec77/plot_four_grids.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_four_grids.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_house_with_colors.html b/auto_examples/drawing/plot_house_with_colors.html index 8fa4b43d..a88ea746 100644 --- a/auto_examples/drawing/plot_house_with_colors.html +++ b/auto_examples/drawing/plot_house_with_colors.html @@ -538,7 +538,7 @@ to download the full example code</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.075 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.070 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-house-with-colors-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/98363b3c011ceaffb10684a5ba5de25b/plot_house_with_colors.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_house_with_colors.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_knuth_miles.html b/auto_examples/drawing/plot_knuth_miles.html index faf231c0..252945ae 100644 --- a/auto_examples/drawing/plot_knuth_miles.html +++ b/auto_examples/drawing/plot_knuth_miles.html @@ -660,7 +660,7 @@ Graph with 128 nodes and 8128 edges <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.100 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.097 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-knuth-miles-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/e921c603ea1764485dc9acff178a2f05/plot_knuth_miles.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_knuth_miles.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_labels_and_colors.html b/auto_examples/drawing/plot_labels_and_colors.html index a2fcec41..fe102824 100644 --- a/auto_examples/drawing/plot_labels_and_colors.html +++ b/auto_examples/drawing/plot_labels_and_colors.html @@ -566,7 +566,7 @@ components of a graph.</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.189 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.179 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-labels-and-colors-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/cff4f78bc18685caa50507ced57e7c6f/plot_labels_and_colors.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_labels_and_colors.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_multipartite_graph.html b/auto_examples/drawing/plot_multipartite_graph.html index 63cdb82d..aa2b3c29 100644 --- a/auto_examples/drawing/plot_multipartite_graph.html +++ b/auto_examples/drawing/plot_multipartite_graph.html @@ -553,7 +553,7 @@ to download the full example code</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.105 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.074 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-multipartite-graph-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/6cb4bf689cf53c849bce13cbab13eaec/plot_multipartite_graph.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_multipartite_graph.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_node_colormap.html b/auto_examples/drawing/plot_node_colormap.html index 5a52a697..c0c2035a 100644 --- a/auto_examples/drawing/plot_node_colormap.html +++ b/auto_examples/drawing/plot_node_colormap.html @@ -526,7 +526,7 @@ to download the full example code</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.052 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.050 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-node-colormap-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/19db6fb1da12c9b9c0afca26691448c8/plot_node_colormap.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_node_colormap.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_rainbow_coloring.html b/auto_examples/drawing/plot_rainbow_coloring.html index 3ee17c7f..d70f19bd 100644 --- a/auto_examples/drawing/plot_rainbow_coloring.html +++ b/auto_examples/drawing/plot_rainbow_coloring.html @@ -578,7 +578,7 @@ helpful in determining how to place the tree copies.</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.129 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.124 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-rainbow-coloring-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/b64fd85d6e5ba509e65b2cb30a8274ed/plot_rainbow_coloring.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_rainbow_coloring.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_random_geometric_graph.html b/auto_examples/drawing/plot_random_geometric_graph.html index 0d204e16..184f15b2 100644 --- a/auto_examples/drawing/plot_random_geometric_graph.html +++ b/auto_examples/drawing/plot_random_geometric_graph.html @@ -555,7 +555,7 @@ to download the full example code</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.106 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.101 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-random-geometric-graph-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/f8f8cacecc651443537b92fc341fba08/plot_random_geometric_graph.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_random_geometric_graph.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_sampson.html b/auto_examples/drawing/plot_sampson.html index c906aa60..e3370279 100644 --- a/auto_examples/drawing/plot_sampson.html +++ b/auto_examples/drawing/plot_sampson.html @@ -557,7 +557,7 @@ to download the full example code</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.257 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.227 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-sampson-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/838bbb120e1c43a61657821eddf29c25/plot_sampson.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_sampson.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_selfloops.html b/auto_examples/drawing/plot_selfloops.html index e3222e2a..8cbfd887 100644 --- a/auto_examples/drawing/plot_selfloops.html +++ b/auto_examples/drawing/plot_selfloops.html @@ -540,7 +540,7 @@ This example shows how to draw self-loops with <code class="xref py py-obj docut <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.081 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.075 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-selfloops-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/b6f62567cb843f23abdd4b7268921c0b/plot_selfloops.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_selfloops.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_simple_path.html b/auto_examples/drawing/plot_simple_path.html index a8c5d3eb..af558e58 100644 --- a/auto_examples/drawing/plot_simple_path.html +++ b/auto_examples/drawing/plot_simple_path.html @@ -526,7 +526,7 @@ to download the full example code</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.058 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.054 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-simple-path-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/2c281c05b18d8d3cf43a312fc3d67a3b/plot_simple_path.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_simple_path.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_spectral_grid.html b/auto_examples/drawing/plot_spectral_grid.html index d92a3d26..7ccd3117 100644 --- a/auto_examples/drawing/plot_spectral_grid.html +++ b/auto_examples/drawing/plot_spectral_grid.html @@ -568,7 +568,7 @@ As you remove internal nodes, this effect increases.</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.230 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.211 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-spectral-grid-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/5479a9bd23bf1ace2ef03c13b4ac9d7f/plot_spectral_grid.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_spectral_grid.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_tsp.html b/auto_examples/drawing/plot_tsp.html index 9e3ce8f7..ad2acc15 100644 --- a/auto_examples/drawing/plot_tsp.html +++ b/auto_examples/drawing/plot_tsp.html @@ -568,7 +568,7 @@ that the traveler has to follow in order to minimize the total cost.</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.089 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.083 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-tsp-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/cc9848c15dd2eeae1872b955a8f34d15/plot_tsp.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_tsp.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_unix_email.html b/auto_examples/drawing/plot_unix_email.html index 01fba44c..29a21ec5 100644 --- a/auto_examples/drawing/plot_unix_email.html +++ b/auto_examples/drawing/plot_unix_email.html @@ -583,7 +583,7 @@ From: ted@com To: alice@edu Subject: get together for lunch to discuss Networks? <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.117 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.145 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-unix-email-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/213697eef7dec7ebca6ee2e064eb9c24/plot_unix_email.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_unix_email.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_weighted_graph.html b/auto_examples/drawing/plot_weighted_graph.html index 0ae4df28..1352a46d 100644 --- a/auto_examples/drawing/plot_weighted_graph.html +++ b/auto_examples/drawing/plot_weighted_graph.html @@ -556,7 +556,7 @@ to download the full example code</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.084 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.079 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-weighted-graph-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/32d3b6ab4dec83957a1981fa91e52e14/plot_weighted_graph.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_weighted_graph.py</span></code></a></p> diff --git a/auto_examples/drawing/sg_execution_times.html b/auto_examples/drawing/sg_execution_times.html index ef901091..93efb927 100644 --- a/auto_examples/drawing/sg_execution_times.html +++ b/auto_examples/drawing/sg_execution_times.html @@ -463,99 +463,99 @@ <section id="computation-times"> <span id="sphx-glr-auto-examples-drawing-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this heading">#</a></h1> -<p><strong>00:04.113</strong> total execution time for <strong>auto_examples_drawing</strong> files:</p> +<p><strong>00:03.831</strong> total execution time for <strong>auto_examples_drawing</strong> files:</p> <table class="table"> <tbody> <tr class="row-odd"><td><p><a class="reference internal" href="plot_eigenvalues.html#sphx-glr-auto-examples-drawing-plot-eigenvalues-py"><span class="std std-ref">Eigenvalues</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_eigenvalues.py</span></code>)</p></td> -<td><p>00:00.698</p></td> +<td><p>00:00.653</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_chess_masters.html#sphx-glr-auto-examples-drawing-plot-chess-masters-py"><span class="std std-ref">Chess Masters</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_chess_masters.py</span></code>)</p></td> -<td><p>00:00.406</p></td> +<td><p>00:00.371</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_four_grids.html#sphx-glr-auto-examples-drawing-plot-four-grids-py"><span class="std std-ref">Four Grids</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_four_grids.py</span></code>)</p></td> -<td><p>00:00.333</p></td> +<td><p>00:00.301</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_custom_node_icons.html#sphx-glr-auto-examples-drawing-plot-custom-node-icons-py"><span class="std std-ref">Custom node icons</span></a> (<code class="docutils literal notranslate"><span 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b/auto_examples/geospatial/sg_execution_times.html @@ -463,27 +463,27 @@ <section id="computation-times"> <span id="sphx-glr-auto-examples-geospatial-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this heading">#</a></h1> -<p><strong>00:16.456</strong> total execution time for <strong>auto_examples_geospatial</strong> files:</p> +<p><strong>00:25.132</strong> total execution time for <strong>auto_examples_geospatial</strong> files:</p> <table class="table"> <tbody> <tr class="row-odd"><td><p><a class="reference internal" href="plot_osmnx.html#sphx-glr-auto-examples-geospatial-plot-osmnx-py"><span class="std std-ref">OpenStreetMap with OSMnx</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_osmnx.py</span></code>)</p></td> -<td><p>00:05.805</p></td> +<td><p>00:14.726</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_delaunay.html#sphx-glr-auto-examples-geospatial-plot-delaunay-py"><span class="std std-ref">Delaunay graphs from geographic points</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_delaunay.py</span></code>)</p></td> -<td><p>00:03.629</p></td> +<td><p>00:03.743</p></td> <td><p>0.0 MB</p></td> </tr> -<tr class="row-odd"><td><p><a class="reference internal" href="plot_points.html#sphx-glr-auto-examples-geospatial-plot-points-py"><span class="std std-ref">Graphs from geographic points</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_points.py</span></code>)</p></td> -<td><p>00:03.349</p></td> +<tr class="row-odd"><td><p><a class="reference internal" href="plot_lines.html#sphx-glr-auto-examples-geospatial-plot-lines-py"><span class="std std-ref">Graphs from a set of lines</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_lines.py</span></code>)</p></td> +<td><p>00:03.254</p></td> <td><p>0.0 MB</p></td> </tr> -<tr class="row-even"><td><p><a class="reference internal" href="plot_lines.html#sphx-glr-auto-examples-geospatial-plot-lines-py"><span class="std std-ref">Graphs from a set of lines</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_lines.py</span></code>)</p></td> -<td><p>00:03.240</p></td> +<tr class="row-even"><td><p><a class="reference internal" href="plot_points.html#sphx-glr-auto-examples-geospatial-plot-points-py"><span class="std std-ref">Graphs from geographic points</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_points.py</span></code>)</p></td> +<td><p>00:02.988</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_polygons.html#sphx-glr-auto-examples-geospatial-plot-polygons-py"><span class="std std-ref">Graphs from Polygons</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_polygons.py</span></code>)</p></td> -<td><p>00:00.433</p></td> +<td><p>00:00.421</p></td> <td><p>0.0 MB</p></td> </tr> </tbody> diff --git a/auto_examples/graph/plot_dag_layout.html b/auto_examples/graph/plot_dag_layout.html index 9999d6a9..c7280c53 100644 --- a/auto_examples/graph/plot_dag_layout.html +++ b/auto_examples/graph/plot_dag_layout.html @@ -541,7 +541,7 @@ order.</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.116 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.110 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graph-plot-dag-layout-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/317508b452046ab7944bed07a87a11a5/plot_dag_layout.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_dag_layout.py</span></code></a></p> diff --git a/auto_examples/graph/plot_degree_sequence.html b/auto_examples/graph/plot_degree_sequence.html index 1fc91a69..645361ca 100644 --- a/auto_examples/graph/plot_degree_sequence.html +++ b/auto_examples/graph/plot_degree_sequence.html @@ -548,7 +548,7 @@ degree #nodes <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.059 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.055 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graph-plot-degree-sequence-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/27102b9986eea2f742603c5d8496d2f8/plot_degree_sequence.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_degree_sequence.py</span></code></a></p> diff --git a/auto_examples/graph/plot_erdos_renyi.html b/auto_examples/graph/plot_erdos_renyi.html index 0f51c672..c532b522 100644 --- a/auto_examples/graph/plot_erdos_renyi.html +++ b/auto_examples/graph/plot_erdos_renyi.html @@ -562,7 +562,7 @@ the adjacency list <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.060 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.056 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graph-plot-erdos-renyi-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/1dae7040b667b61c3253579b3b21fe83/plot_erdos_renyi.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_erdos_renyi.py</span></code></a></p> diff --git a/auto_examples/graph/plot_expected_degree_sequence.html b/auto_examples/graph/plot_expected_degree_sequence.html index 09bb4b4a..78b976ef 100644 --- a/auto_examples/graph/plot_expected_degree_sequence.html +++ b/auto_examples/graph/plot_expected_degree_sequence.html @@ -538,46 +538,49 @@ degree (#nodes) **** 28 ( 0) 29 ( 0) 30 ( 0) -31 ( 1) * +31 ( 0) 32 ( 0) 33 ( 0) -34 ( 0) -35 ( 0) -36 ( 0) -37 ( 5) ***** -38 ( 2) ** -39 ( 7) ******* -40 ( 8) ******** +34 ( 1) * +35 ( 1) * +36 ( 5) ***** +37 ( 7) ******* +38 ( 8) ******** +39 ( 9) ********* +40 ( 3) *** 41 (16) **************** -42 (12) ************ +42 ( 6) ****** 43 (17) ***************** -44 (25) ************************* -45 (25) ************************* -46 (21) ********************* -47 (21) ********************* -48 (26) ************************** -49 (29) ***************************** -50 (33) ********************************* -51 (35) *********************************** -52 (18) ****************** -53 (35) *********************************** -54 (30) ****************************** -55 (20) ******************** -56 (23) *********************** -57 (21) ********************* -58 (19) ******************* -59 (13) ************* +44 (19) ******************* +45 (22) ********************** +46 (25) ************************* +47 (25) ************************* +48 (31) ******************************* +49 (35) *********************************** +50 (34) ********************************** +51 (24) ************************ +52 (31) ******************************* +53 (34) ********************************** +54 (26) ************************** +55 (32) ******************************** +56 (16) **************** +57 (10) ********** +58 (14) ************** +59 (10) ********** 60 (13) ************* -61 (10) ********** -62 ( 2) ** -63 ( 2) ** -64 ( 3) *** -65 ( 2) ** -66 ( 3) *** +61 ( 4) **** +62 ( 6) ****** +63 ( 1) * +64 ( 4) **** +65 ( 3) *** +66 ( 5) ***** 67 ( 0) -68 ( 1) * +68 ( 0) 69 ( 1) * 70 ( 1) * +71 ( 0) +72 ( 0) +73 ( 1) * </pre></div> </div> <div class="line-block"> @@ -597,7 +600,7 @@ degree (#nodes) **** <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">i</span></a><span class="si">:</span><span class="s2">2</span><span class="si">}</span><span class="s2"> (</span><span class="si">{</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">d</span></a><span class="si">:</span><span class="s2">2</span><span class="si">}</span><span class="s2">) </span><span class="si">{</span><span class="s1">'*'</span><span class="o">*</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">d</span></a><span class="si">}</span><span class="s2">"</span><span class="p">)</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.031 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.030 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graph-plot-expected-degree-sequence-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/7378087382f40e96e66bce4a35ba0e52/plot_expected_degree_sequence.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_expected_degree_sequence.py</span></code></a></p> diff --git a/auto_examples/graph/plot_football.html b/auto_examples/graph/plot_football.html index 75b945d2..9ece2f8e 100644 --- a/auto_examples/graph/plot_football.html +++ b/auto_examples/graph/plot_football.html @@ -686,7 +686,7 @@ Hawaii 11 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.339 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.341 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graph-plot-football-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/ca0a30060f60faf520286faa348f4700/plot_football.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_football.py</span></code></a></p> diff --git a/auto_examples/graph/plot_karate_club.html b/auto_examples/graph/plot_karate_club.html index b9ae1b93..cc319240 100644 --- a/auto_examples/graph/plot_karate_club.html +++ b/auto_examples/graph/plot_karate_club.html @@ -562,7 +562,7 @@ Journal of Anthropological Research, 33, 452-473.</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.089 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.084 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graph-plot-karate-club-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/373a1e407e4caee6fc7b7b46704a985c/plot_karate_club.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_karate_club.py</span></code></a></p> diff --git a/auto_examples/graph/plot_morse_trie.html b/auto_examples/graph/plot_morse_trie.html index c399f60c..854a516d 100644 --- a/auto_examples/graph/plot_morse_trie.html +++ b/auto_examples/graph/plot_morse_trie.html @@ -602,7 +602,7 @@ the path.</p> <span class="nb">print</span><span class="p">(</span><span class="s2">" "</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="n">morse_encode</span><span class="p">(</span><span class="n">ltr</span><span class="p">)</span> <span class="k">for</span> <span class="n">ltr</span> <span class="ow">in</span> <span class="s2">"ilovenetworkx"</span><span class="p">]))</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.181 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.168 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graph-plot-morse-trie-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/60379a4283563d425090aaae07ab115a/plot_morse_trie.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_morse_trie.py</span></code></a></p> diff --git a/auto_examples/graph/plot_napoleon_russian_campaign.html b/auto_examples/graph/plot_napoleon_russian_campaign.html index d1776ad6..4a354b21 100644 --- a/auto_examples/graph/plot_napoleon_russian_campaign.html +++ b/auto_examples/graph/plot_napoleon_russian_campaign.html @@ -632,7 +632,7 @@ to download the full example code</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.132 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.120 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graph-plot-napoleon-russian-campaign-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/87e75a2d09fb817a4616bb71aa44546f/plot_napoleon_russian_campaign.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_napoleon_russian_campaign.py</span></code></a></p> diff --git a/auto_examples/graph/plot_roget.html b/auto_examples/graph/plot_roget.html index c8cdc73d..d25a8e19 100644 --- a/auto_examples/graph/plot_roget.html +++ b/auto_examples/graph/plot_roget.html @@ -588,7 +588,7 @@ DiGraph with 1022 nodes and 5075 edges <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.243 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.226 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graph-plot-roget-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/118b3a0c87610e4910d74143c904d290/plot_roget.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_roget.py</span></code></a></p> diff --git a/auto_examples/graph/plot_triad_types.html b/auto_examples/graph/plot_triad_types.html index b7411e47..1d35f5d1 100644 --- a/auto_examples/graph/plot_triad_types.html +++ b/auto_examples/graph/plot_triad_types.html @@ -563,7 +563,7 @@ the Orientation as Up (U), Down (D) , Cyclical (C) or Transitive (T).</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 1.098 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 1.021 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graph-plot-triad-types-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/b7a826e19c8bd8bafecaae1ae69c7d1d/plot_triad_types.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_triad_types.py</span></code></a></p> diff --git a/auto_examples/graph/plot_words.html b/auto_examples/graph/plot_words.html index 4fb535b6..bb96efbd 100644 --- a/auto_examples/graph/plot_words.html +++ b/auto_examples/graph/plot_words.html @@ -624,7 +624,7 @@ None <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.417 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.354 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graph-plot-words-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/e6a489a8b2deb49ed237fac38a28f429/plot_words.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_words.py</span></code></a></p> diff --git a/auto_examples/graph/sg_execution_times.html b/auto_examples/graph/sg_execution_times.html index 719f7d4f..ef8e5775 100644 --- a/auto_examples/graph/sg_execution_times.html +++ b/auto_examples/graph/sg_execution_times.html @@ -463,51 +463,51 @@ <section id="computation-times"> <span id="sphx-glr-auto-examples-graph-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this heading">#</a></h1> -<p><strong>00:02.765</strong> total execution time for <strong>auto_examples_graph</strong> files:</p> +<p><strong>00:02.565</strong> total execution time for <strong>auto_examples_graph</strong> files:</p> <table class="table"> <tbody> <tr class="row-odd"><td><p><a class="reference internal" href="plot_triad_types.html#sphx-glr-auto-examples-graph-plot-triad-types-py"><span class="std std-ref">Triads</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_triad_types.py</span></code>)</p></td> -<td><p>00:01.098</p></td> +<td><p>00:01.021</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_words.html#sphx-glr-auto-examples-graph-plot-words-py"><span class="std std-ref">Words/Ladder Graph</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_words.py</span></code>)</p></td> -<td><p>00:00.417</p></td> +<td><p>00:00.354</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_football.html#sphx-glr-auto-examples-graph-plot-football-py"><span class="std std-ref">Football</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_football.py</span></code>)</p></td> -<td><p>00:00.339</p></td> +<td><p>00:00.341</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_roget.html#sphx-glr-auto-examples-graph-plot-roget-py"><span class="std std-ref">Roget</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_roget.py</span></code>)</p></td> -<td><p>00:00.243</p></td> +<td><p>00:00.226</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_morse_trie.html#sphx-glr-auto-examples-graph-plot-morse-trie-py"><span class="std std-ref">Morse Trie</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_morse_trie.py</span></code>)</p></td> -<td><p>00:00.181</p></td> +<td><p>00:00.168</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_napoleon_russian_campaign.html#sphx-glr-auto-examples-graph-plot-napoleon-russian-campaign-py"><span class="std std-ref">Napoleon Russian Campaign</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_napoleon_russian_campaign.py</span></code>)</p></td> -<td><p>00:00.132</p></td> +<td><p>00:00.120</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_dag_layout.html#sphx-glr-auto-examples-graph-plot-dag-layout-py"><span class="std std-ref">DAG - Topological Layout</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_dag_layout.py</span></code>)</p></td> -<td><p>00:00.116</p></td> +<td><p>00:00.110</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_karate_club.html#sphx-glr-auto-examples-graph-plot-karate-club-py"><span class="std std-ref">Karate Club</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_karate_club.py</span></code>)</p></td> -<td><p>00:00.089</p></td> +<td><p>00:00.084</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_erdos_renyi.html#sphx-glr-auto-examples-graph-plot-erdos-renyi-py"><span class="std std-ref">Erdos Renyi</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_erdos_renyi.py</span></code>)</p></td> -<td><p>00:00.060</p></td> +<td><p>00:00.056</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_degree_sequence.html#sphx-glr-auto-examples-graph-plot-degree-sequence-py"><span class="std std-ref">Degree Sequence</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_degree_sequence.py</span></code>)</p></td> -<td><p>00:00.059</p></td> +<td><p>00:00.055</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_expected_degree_sequence.html#sphx-glr-auto-examples-graph-plot-expected-degree-sequence-py"><span class="std std-ref">Expected Degree Sequence</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_expected_degree_sequence.py</span></code>)</p></td> -<td><p>00:00.031</p></td> +<td><p>00:00.030</p></td> <td><p>0.0 MB</p></td> </tr> </tbody> diff --git a/auto_examples/graphviz_drawing/plot_attributes.html b/auto_examples/graphviz_drawing/plot_attributes.html index ec4c31c4..b6dea393 100644 --- a/auto_examples/graphviz_drawing/plot_attributes.html +++ b/auto_examples/graphviz_drawing/plot_attributes.html @@ -532,7 +532,7 @@ node node attributes <span class="nb">print</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">nodes</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="kc">True</span><span class="p">))</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.028 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.064 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graphviz-drawing-plot-attributes-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/52bb0ebd52824aa460a3ecb45c1cb5e5/plot_attributes.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_attributes.py</span></code></a></p> diff --git a/auto_examples/graphviz_drawing/plot_conversion.html b/auto_examples/graphviz_drawing/plot_conversion.html index a45c8d14..8d898868 100644 --- a/auto_examples/graphviz_drawing/plot_conversion.html +++ b/auto_examples/graphviz_drawing/plot_conversion.html @@ -514,7 +514,7 @@ to download the full example code</p> <a href="https://pygraphviz.github.io/documentation/stable/reference/agraph.html#pygraphviz.AGraph.draw" title="pygraphviz.AGraph.draw" class="sphx-glr-backref-module-pygraphviz sphx-glr-backref-type-py-method"><span class="n">A</span><span class="o">.</span><span class="n">draw</span></a><span class="p">(</span><span class="s2">"k5.png"</span><span class="p">,</span> <span class="n">prog</span><span class="o">=</span><span class="s2">"neato"</span><span class="p">)</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.025 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.024 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graphviz-drawing-plot-conversion-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/27aa0c08bacf20ba3f5ce4f8d02ac226/plot_conversion.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_conversion.py</span></code></a></p> diff --git a/auto_examples/graphviz_drawing/plot_grid.html b/auto_examples/graphviz_drawing/plot_grid.html index 5daf25fd..f8ade20a 100644 --- a/auto_examples/graphviz_drawing/plot_grid.html +++ b/auto_examples/graphviz_drawing/plot_grid.html @@ -519,7 +519,7 @@ Graphviz command line interface to create visualizations.</p> <img src="../../_images/sphx_glr_plot_grid_001.png" srcset="../../_images/sphx_glr_plot_grid_001.png" alt="plot grid" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Now run: neato -Tps grid.dot >grid.ps </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.068 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.067 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graphviz-drawing-plot-grid-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/26e3cd745ae317a76a0df34cbf4999d8/plot_grid.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_grid.py</span></code></a></p> diff --git a/auto_examples/graphviz_drawing/plot_mini_atlas.html b/auto_examples/graphviz_drawing/plot_mini_atlas.html index 21820239..386e69c7 100644 --- a/auto_examples/graphviz_drawing/plot_mini_atlas.html +++ b/auto_examples/graphviz_drawing/plot_mini_atlas.html @@ -543,7 +543,7 @@ Graph named 'G19' with 5 nodes and 0 edges <a href="https://pygraphviz.github.io/documentation/stable/reference/agraph.html#pygraphviz.AGraph.draw" title="pygraphviz.AGraph.draw" class="sphx-glr-backref-module-pygraphviz sphx-glr-backref-type-py-method"><span class="n">A</span><span class="o">.</span><span class="n">draw</span></a><span class="p">(</span><span class="s2">"A20.png"</span><span class="p">,</span> <span class="n">prog</span><span class="o">=</span><span class="s2">"neato"</span><span class="p">)</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.079 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.075 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graphviz-drawing-plot-mini-atlas-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/cc271806f4fdfe8710206c593b90e506/plot_mini_atlas.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_mini_atlas.py</span></code></a></p> diff --git a/auto_examples/graphviz_drawing/sg_execution_times.html b/auto_examples/graphviz_drawing/sg_execution_times.html index f87c63e9..742ba524 100644 --- a/auto_examples/graphviz_drawing/sg_execution_times.html +++ b/auto_examples/graphviz_drawing/sg_execution_times.html @@ -463,23 +463,23 @@ <section id="computation-times"> <span id="sphx-glr-auto-examples-graphviz-drawing-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this heading">#</a></h1> -<p><strong>00:00.201</strong> total execution time for <strong>auto_examples_graphviz_drawing</strong> files:</p> +<p><strong>00:00.230</strong> total execution time for <strong>auto_examples_graphviz_drawing</strong> files:</p> <table class="table"> <tbody> <tr class="row-odd"><td><p><a class="reference internal" href="plot_mini_atlas.html#sphx-glr-auto-examples-graphviz-drawing-plot-mini-atlas-py"><span class="std std-ref">Atlas</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_mini_atlas.py</span></code>)</p></td> -<td><p>00:00.079</p></td> +<td><p>00:00.075</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_grid.html#sphx-glr-auto-examples-graphviz-drawing-plot-grid-py"><span class="std std-ref">2D Grid</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_grid.py</span></code>)</p></td> -<td><p>00:00.068</p></td> +<td><p>00:00.067</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_attributes.html#sphx-glr-auto-examples-graphviz-drawing-plot-attributes-py"><span class="std std-ref">Attributes</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_attributes.py</span></code>)</p></td> -<td><p>00:00.028</p></td> +<td><p>00:00.064</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_conversion.html#sphx-glr-auto-examples-graphviz-drawing-plot-conversion-py"><span class="std std-ref">Conversion</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_conversion.py</span></code>)</p></td> -<td><p>00:00.025</p></td> +<td><p>00:00.024</p></td> <td><p>0.0 MB</p></td> </tr> </tbody> diff --git a/auto_examples/graphviz_layout/plot_atlas.html b/auto_examples/graphviz_layout/plot_atlas.html index 1b6d48fe..be2afb7a 100644 --- a/auto_examples/graphviz_layout/plot_atlas.html +++ b/auto_examples/graphviz_layout/plot_atlas.html @@ -549,7 +549,7 @@ We don’t plot the empty graph nor the single node graph. <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 3.711 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 3.571 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graphviz-layout-plot-atlas-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/37c712582f2a7575f32a59a1389228a7/plot_atlas.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_atlas.py</span></code></a></p> diff --git a/auto_examples/graphviz_layout/plot_circular_tree.html b/auto_examples/graphviz_layout/plot_circular_tree.html index 6403bd07..78048af9 100644 --- a/auto_examples/graphviz_layout/plot_circular_tree.html +++ b/auto_examples/graphviz_layout/plot_circular_tree.html @@ -510,7 +510,7 @@ to download the full example code</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.150 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.145 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graphviz-layout-plot-circular-tree-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/e854482dd498b1c5f7f158a5717b999d/plot_circular_tree.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_circular_tree.py</span></code></a></p> diff --git a/auto_examples/graphviz_layout/plot_decomposition.html b/auto_examples/graphviz_layout/plot_decomposition.html index aa8d8c93..0daffa26 100644 --- a/auto_examples/graphviz_layout/plot_decomposition.html +++ b/auto_examples/graphviz_layout/plot_decomposition.html @@ -535,7 +535,7 @@ to download the full example code</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.301 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.281 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graphviz-layout-plot-decomposition-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/533257c084adfbb38066f806a87784c5/plot_decomposition.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_decomposition.py</span></code></a></p> diff --git a/auto_examples/graphviz_layout/plot_giant_component.html b/auto_examples/graphviz_layout/plot_giant_component.html index 55398b85..92f71834 100644 --- a/auto_examples/graphviz_layout/plot_giant_component.html +++ b/auto_examples/graphviz_layout/plot_giant_component.html @@ -543,7 +543,7 @@ giant connected component in a binomial random graph.</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.805 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.775 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graphviz-layout-plot-giant-component-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/f5d29b33ff492f40e4749050b3f5e7dd/plot_giant_component.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_giant_component.py</span></code></a></p> diff --git a/auto_examples/graphviz_layout/plot_lanl_routes.html b/auto_examples/graphviz_layout/plot_lanl_routes.html index ab42b498..d4ed3f89 100644 --- a/auto_examples/graphviz_layout/plot_lanl_routes.html +++ b/auto_examples/graphviz_layout/plot_lanl_routes.html @@ -561,7 +561,7 @@ to download the full example code</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.339 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.327 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graphviz-layout-plot-lanl-routes-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/30e04b92b8aefc7afe7f634d84ae925a/plot_lanl_routes.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_lanl_routes.py</span></code></a></p> diff --git a/auto_examples/graphviz_layout/sg_execution_times.html b/auto_examples/graphviz_layout/sg_execution_times.html index d69ba8ba..6316c4b2 100644 --- a/auto_examples/graphviz_layout/sg_execution_times.html +++ b/auto_examples/graphviz_layout/sg_execution_times.html @@ -463,27 +463,27 @@ <section id="computation-times"> <span id="sphx-glr-auto-examples-graphviz-layout-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this heading">#</a></h1> -<p><strong>00:05.307</strong> total execution time for <strong>auto_examples_graphviz_layout</strong> files:</p> +<p><strong>00:05.099</strong> total execution time for <strong>auto_examples_graphviz_layout</strong> files:</p> <table class="table"> <tbody> <tr class="row-odd"><td><p><a class="reference internal" href="plot_atlas.html#sphx-glr-auto-examples-graphviz-layout-plot-atlas-py"><span class="std std-ref">Atlas</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_atlas.py</span></code>)</p></td> -<td><p>00:03.711</p></td> +<td><p>00:03.571</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_giant_component.html#sphx-glr-auto-examples-graphviz-layout-plot-giant-component-py"><span class="std std-ref">Giant Component</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_giant_component.py</span></code>)</p></td> -<td><p>00:00.805</p></td> +<td><p>00:00.775</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_lanl_routes.html#sphx-glr-auto-examples-graphviz-layout-plot-lanl-routes-py"><span class="std std-ref">Lanl Routes</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_lanl_routes.py</span></code>)</p></td> -<td><p>00:00.339</p></td> +<td><p>00:00.327</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_decomposition.html#sphx-glr-auto-examples-graphviz-layout-plot-decomposition-py"><span class="std std-ref">Decomposition</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_decomposition.py</span></code>)</p></td> -<td><p>00:00.301</p></td> +<td><p>00:00.281</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_circular_tree.html#sphx-glr-auto-examples-graphviz-layout-plot-circular-tree-py"><span class="std std-ref">Circular Tree</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_circular_tree.py</span></code>)</p></td> -<td><p>00:00.150</p></td> +<td><p>00:00.145</p></td> <td><p>0.0 MB</p></td> </tr> </tbody> diff --git a/auto_examples/subclass/plot_antigraph.html b/auto_examples/subclass/plot_antigraph.html index 8538166e..7c23ee3f 100644 --- a/auto_examples/subclass/plot_antigraph.html +++ b/auto_examples/subclass/plot_antigraph.html @@ -680,7 +680,7 @@ algorithms.</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.091 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.088 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-subclass-plot-antigraph-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/652afbfc3c52c8cdd7689321df2e696a/plot_antigraph.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_antigraph.py</span></code></a></p> diff --git a/auto_examples/subclass/plot_printgraph.html b/auto_examples/subclass/plot_printgraph.html index e63ffb1e..1ca92436 100644 --- a/auto_examples/subclass/plot_printgraph.html +++ b/auto_examples/subclass/plot_printgraph.html @@ -616,7 +616,7 @@ Add edge: 9-12 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.065 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.055 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-subclass-plot-printgraph-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/1b5e7bf8d2514d71280314171170de85/plot_printgraph.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_printgraph.py</span></code></a></p> diff --git a/auto_examples/subclass/sg_execution_times.html b/auto_examples/subclass/sg_execution_times.html index 83e37599..821daedb 100644 --- a/auto_examples/subclass/sg_execution_times.html +++ b/auto_examples/subclass/sg_execution_times.html @@ -463,15 +463,15 @@ <section id="computation-times"> <span id="sphx-glr-auto-examples-subclass-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this heading">#</a></h1> -<p><strong>00:00.156</strong> total execution time for <strong>auto_examples_subclass</strong> files:</p> +<p><strong>00:00.142</strong> total execution time for <strong>auto_examples_subclass</strong> files:</p> <table class="table"> <tbody> <tr class="row-odd"><td><p><a class="reference internal" href="plot_antigraph.html#sphx-glr-auto-examples-subclass-plot-antigraph-py"><span class="std std-ref">Antigraph</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_antigraph.py</span></code>)</p></td> -<td><p>00:00.091</p></td> +<td><p>00:00.088</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_printgraph.html#sphx-glr-auto-examples-subclass-plot-printgraph-py"><span class="std std-ref">Print Graph</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_printgraph.py</span></code>)</p></td> -<td><p>00:00.065</p></td> +<td><p>00:00.055</p></td> <td><p>0.0 MB</p></td> </tr> </tbody> diff --git a/developer/index.html b/developer/index.html index 903c88d6..bcb7e2b1 100644 --- a/developer/index.html +++ b/developer/index.html @@ -493,7 +493,7 @@ <dd class="field-odd"><p>3.0rc2.dev0</p> </dd> <dt class="field-even">Date<span class="colon">:</span></dt> -<dd class="field-even"><p>Dec 14, 2022</p> +<dd class="field-even"><p>Dec 15, 2022</p> </dd> </dl> <div class="toctree-wrapper compound"> @@ -464,7 +464,7 @@ <dd class="field-odd"><p>3.0rc2.dev0</p> </dd> <dt class="field-even">Date<span class="colon">:</span></dt> -<dd class="field-even"><p>Dec 14, 2022</p> +<dd class="field-even"><p>Dec 15, 2022</p> </dd> </dl> <p>NetworkX is a Python package for the creation, manipulation, and study diff --git a/objects.inv b/objects.inv Binary files differindex 60bf2da2..2c8bc0fb 100644 --- a/objects.inv +++ b/objects.inv diff --git a/reference/index.html b/reference/index.html index 0a3ea7d0..a9940f00 100644 --- a/reference/index.html +++ b/reference/index.html @@ -496,7 +496,7 @@ <dd class="field-odd"><p>3.0rc2.dev0</p> </dd> <dt class="field-even">Date<span class="colon">:</span></dt> -<dd class="field-even"><p>Dec 14, 2022</p> +<dd class="field-even"><p>Dec 15, 2022</p> </dd> </dl> </div></blockquote> diff --git a/reference/introduction-7.hires.png b/reference/introduction-7.hires.png Binary files differindex 83f07105..53cc5d7a 100644 --- a/reference/introduction-7.hires.png +++ b/reference/introduction-7.hires.png diff --git a/reference/introduction-7.pdf b/reference/introduction-7.pdf Binary files differindex 952cd809..fbfb1345 100644 --- a/reference/introduction-7.pdf +++ b/reference/introduction-7.pdf diff --git a/reference/introduction-7.png b/reference/introduction-7.png Binary files differindex 164c0006..8bb8edae 100644 --- a/reference/introduction-7.png +++ b/reference/introduction-7.png diff --git a/reference/introduction.ipynb b/reference/introduction.ipynb index c992f273..91fb240d 100644 --- a/reference/introduction.ipynb +++ b/reference/introduction.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "c88afbd5", + "id": "78882f52", "metadata": {}, "source": [ "## Introduction\n", @@ -34,7 +34,7 @@ { "cell_type": "code", "execution_count": null, - "id": "37692d81", + "id": "b1e0dcc9", "metadata": {}, "outputs": [], "source": [ @@ -43,7 +43,7 @@ }, { "cell_type": "markdown", - "id": "ac42d6a2", + "id": "5dfeb316", "metadata": {}, "source": [ "To save repetition, in the documentation we assume that\n", @@ -82,7 +82,7 @@ { "cell_type": "code", "execution_count": null, - "id": "3ebfc7eb", + "id": "f036b34a", "metadata": {}, "outputs": [], "source": [ @@ -94,7 +94,7 @@ }, { "cell_type": "markdown", - "id": "57bc3f5f", + "id": "62377211", "metadata": {}, "source": [ "All graph classes allow any [hashable](https://docs.python.org/3/glossary.html#term-hashable) object as a node.\n", @@ -193,7 +193,7 @@ { "cell_type": "code", "execution_count": null, - "id": "36bf8f12", + "id": "35fc0967", "metadata": {}, "outputs": [], "source": [ @@ -205,7 +205,7 @@ }, { "cell_type": "markdown", - "id": "eda59355", + "id": "b59836b4", "metadata": {}, "source": [ "Edge attributes can be anything:" @@ -214,7 +214,7 @@ { "cell_type": "code", "execution_count": null, - "id": "4a1d901f", + "id": "a0af949c", "metadata": {}, "outputs": [], "source": [ @@ -225,7 +225,7 @@ }, { "cell_type": "markdown", - "id": "20bfe7e0", + "id": "0b169000", "metadata": {}, "source": [ "You can add many edges at one time:" @@ -234,7 +234,7 @@ { "cell_type": "code", "execution_count": null, - "id": "338b6d96", + "id": "af2e02a5", "metadata": {}, "outputs": [], "source": [ @@ -246,7 +246,7 @@ }, { "cell_type": "markdown", - "id": "4892385b", + "id": "0f49dfeb", "metadata": {}, "source": [ "See the Tutorial for more examples.\n", @@ -311,7 +311,7 @@ { "cell_type": "code", "execution_count": null, - "id": "49927773", + "id": "d99a99dd", "metadata": {}, "outputs": [], "source": [ @@ -323,7 +323,7 @@ }, { "cell_type": "markdown", - "id": "0cf697e9", + "id": "98b46216", "metadata": {}, "source": [ "# Drawing\n", @@ -344,7 +344,7 @@ { "cell_type": "code", "execution_count": null, - "id": "6a3cde4f", + "id": "11a0be27", "metadata": {}, "outputs": [], "source": [ @@ -358,7 +358,7 @@ }, { "cell_type": "markdown", - "id": "032623c0", + "id": "a3e7daf4", "metadata": {}, "source": [ "See the examples for more ideas.\n", @@ -398,7 +398,7 @@ { "cell_type": "code", "execution_count": null, - "id": "d53d92e7", + "id": "2af1ffc6", "metadata": {}, "outputs": [], "source": [ @@ -410,7 +410,7 @@ }, { "cell_type": "markdown", - "id": "28f41ebb", + "id": "95c97c18", "metadata": {}, "source": [ "The data structure gets morphed slightly for each base graph class.\n", @@ -428,7 +428,7 @@ { "cell_type": "code", "execution_count": null, - "id": "bdd117df", + "id": "fbdfaf69", "metadata": {}, "outputs": [], "source": [ diff --git a/reference/introduction_full.ipynb b/reference/introduction_full.ipynb index 958e3b2b..a153c1db 100644 --- a/reference/introduction_full.ipynb +++ b/reference/introduction_full.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "c88afbd5", + "id": "78882f52", "metadata": {}, "source": [ "## Introduction\n", @@ -34,13 +34,13 @@ { "cell_type": "code", "execution_count": 1, - "id": "37692d81", + "id": "b1e0dcc9", "metadata": { "execution": { - "iopub.execute_input": "2022-12-14T17:19:48.853645Z", - "iopub.status.busy": "2022-12-14T17:19:48.853262Z", - "iopub.status.idle": "2022-12-14T17:19:48.928074Z", - "shell.execute_reply": "2022-12-14T17:19:48.927381Z" + "iopub.execute_input": "2022-12-15T16:09:57.025156Z", + "iopub.status.busy": "2022-12-15T16:09:57.024939Z", + "iopub.status.idle": "2022-12-15T16:09:57.095130Z", + "shell.execute_reply": "2022-12-15T16:09:57.094503Z" } }, "outputs": [], @@ -50,7 +50,7 @@ }, { "cell_type": "markdown", - "id": "ac42d6a2", + "id": "5dfeb316", "metadata": {}, "source": [ "To save repetition, in the documentation we assume that\n", @@ -89,13 +89,13 @@ { "cell_type": "code", "execution_count": 2, - "id": "3ebfc7eb", + "id": "f036b34a", "metadata": { "execution": { - "iopub.execute_input": "2022-12-14T17:19:48.932024Z", - "iopub.status.busy": "2022-12-14T17:19:48.931776Z", - "iopub.status.idle": "2022-12-14T17:19:48.935270Z", - "shell.execute_reply": "2022-12-14T17:19:48.934632Z" + "iopub.execute_input": "2022-12-15T16:09:57.098384Z", + "iopub.status.busy": "2022-12-15T16:09:57.098162Z", + "iopub.status.idle": "2022-12-15T16:09:57.101457Z", + "shell.execute_reply": "2022-12-15T16:09:57.100828Z" } }, "outputs": [], @@ -108,7 +108,7 @@ }, { "cell_type": "markdown", - "id": "57bc3f5f", + "id": "62377211", "metadata": {}, "source": [ "All graph classes allow any [hashable](https://docs.python.org/3/glossary.html#term-hashable) object as a node.\n", @@ -207,13 +207,13 @@ { "cell_type": "code", "execution_count": 3, - "id": "36bf8f12", + "id": "35fc0967", "metadata": { "execution": { - "iopub.execute_input": "2022-12-14T17:19:48.938430Z", - "iopub.status.busy": "2022-12-14T17:19:48.938212Z", - "iopub.status.idle": "2022-12-14T17:19:48.941744Z", - "shell.execute_reply": "2022-12-14T17:19:48.941095Z" + "iopub.execute_input": "2022-12-15T16:09:57.104166Z", + "iopub.status.busy": "2022-12-15T16:09:57.103954Z", + "iopub.status.idle": "2022-12-15T16:09:57.107235Z", + "shell.execute_reply": "2022-12-15T16:09:57.106619Z" } }, "outputs": [], @@ -226,7 +226,7 @@ }, { "cell_type": "markdown", - "id": "eda59355", + "id": "b59836b4", "metadata": {}, "source": [ "Edge attributes can be anything:" @@ -235,13 +235,13 @@ { "cell_type": "code", "execution_count": 4, - "id": "4a1d901f", + "id": "a0af949c", "metadata": { "execution": { - "iopub.execute_input": "2022-12-14T17:19:48.944837Z", - "iopub.status.busy": "2022-12-14T17:19:48.944620Z", - "iopub.status.idle": "2022-12-14T17:19:48.947791Z", - "shell.execute_reply": "2022-12-14T17:19:48.947157Z" + "iopub.execute_input": "2022-12-15T16:09:57.110117Z", + "iopub.status.busy": "2022-12-15T16:09:57.109918Z", + "iopub.status.idle": "2022-12-15T16:09:57.113026Z", + "shell.execute_reply": "2022-12-15T16:09:57.112424Z" } }, "outputs": [], @@ -253,7 +253,7 @@ }, { "cell_type": "markdown", - "id": "20bfe7e0", + "id": "0b169000", "metadata": {}, "source": [ "You can add many edges at one time:" @@ -262,13 +262,13 @@ { "cell_type": "code", "execution_count": 5, - "id": "338b6d96", + "id": "af2e02a5", "metadata": { "execution": { - "iopub.execute_input": "2022-12-14T17:19:48.950861Z", - "iopub.status.busy": "2022-12-14T17:19:48.950647Z", - "iopub.status.idle": "2022-12-14T17:19:48.954581Z", - "shell.execute_reply": "2022-12-14T17:19:48.953938Z" + "iopub.execute_input": "2022-12-15T16:09:57.115703Z", + "iopub.status.busy": "2022-12-15T16:09:57.115492Z", + "iopub.status.idle": "2022-12-15T16:09:57.119229Z", + "shell.execute_reply": "2022-12-15T16:09:57.118617Z" } }, "outputs": [], @@ -281,7 +281,7 @@ }, { "cell_type": "markdown", - "id": "4892385b", + "id": "0f49dfeb", "metadata": {}, "source": [ "See the Tutorial for more examples.\n", @@ -346,13 +346,13 @@ { "cell_type": "code", "execution_count": 6, - "id": "49927773", + "id": "d99a99dd", "metadata": { "execution": { - "iopub.execute_input": "2022-12-14T17:19:48.957605Z", - "iopub.status.busy": "2022-12-14T17:19:48.957394Z", - "iopub.status.idle": "2022-12-14T17:19:48.961733Z", - "shell.execute_reply": "2022-12-14T17:19:48.961102Z" + "iopub.execute_input": "2022-12-15T16:09:57.122042Z", + "iopub.status.busy": "2022-12-15T16:09:57.121840Z", + "iopub.status.idle": "2022-12-15T16:09:57.126072Z", + "shell.execute_reply": "2022-12-15T16:09:57.125445Z" } }, "outputs": [ @@ -373,7 +373,7 @@ }, { "cell_type": "markdown", - "id": "0cf697e9", + "id": "98b46216", "metadata": {}, "source": [ "# Drawing\n", @@ -394,19 +394,19 @@ { "cell_type": "code", "execution_count": 7, - "id": "6a3cde4f", + "id": "11a0be27", "metadata": { "execution": { - "iopub.execute_input": "2022-12-14T17:19:48.966408Z", - "iopub.status.busy": "2022-12-14T17:19:48.966187Z", - "iopub.status.idle": "2022-12-14T17:19:49.545192Z", - "shell.execute_reply": "2022-12-14T17:19:49.543040Z" + "iopub.execute_input": "2022-12-15T16:09:57.130192Z", + "iopub.status.busy": "2022-12-15T16:09:57.129981Z", + "iopub.status.idle": "2022-12-15T16:09:57.669598Z", + "shell.execute_reply": "2022-12-15T16:09:57.667825Z" } }, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "<Figure size 640x480 with 2 Axes>" ] @@ -426,7 +426,7 @@ }, { "cell_type": "markdown", - "id": "032623c0", + "id": "a3e7daf4", "metadata": {}, "source": [ "See the examples for more ideas.\n", @@ -466,13 +466,13 @@ { "cell_type": "code", "execution_count": 8, - "id": "d53d92e7", + "id": "2af1ffc6", "metadata": { "execution": { - "iopub.execute_input": "2022-12-14T17:19:49.548837Z", - "iopub.status.busy": "2022-12-14T17:19:49.548421Z", - "iopub.status.idle": "2022-12-14T17:19:49.552473Z", - "shell.execute_reply": "2022-12-14T17:19:49.551811Z" + "iopub.execute_input": "2022-12-15T16:09:57.672801Z", + "iopub.status.busy": "2022-12-15T16:09:57.672448Z", + "iopub.status.idle": "2022-12-15T16:09:57.676336Z", + "shell.execute_reply": "2022-12-15T16:09:57.675684Z" } }, "outputs": [ @@ -493,7 +493,7 @@ }, { "cell_type": "markdown", - "id": "28f41ebb", + "id": "95c97c18", "metadata": {}, "source": [ "The data structure gets morphed slightly for each base graph class.\n", @@ -511,13 +511,13 @@ { "cell_type": "code", "execution_count": 9, - "id": "bdd117df", + "id": "fbdfaf69", "metadata": { "execution": { - "iopub.execute_input": "2022-12-14T17:19:49.556358Z", - "iopub.status.busy": "2022-12-14T17:19:49.556136Z", - "iopub.status.idle": "2022-12-14T17:19:49.560264Z", - "shell.execute_reply": "2022-12-14T17:19:49.559612Z" + "iopub.execute_input": "2022-12-15T16:09:57.680069Z", + "iopub.status.busy": "2022-12-15T16:09:57.679850Z", + "iopub.status.idle": "2022-12-15T16:09:57.683843Z", + "shell.execute_reply": "2022-12-15T16:09:57.683217Z" } }, "outputs": [ @@ -549,7 +549,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/release/migration_guide_from_2.x_to_3.0.html b/release/migration_guide_from_2.x_to_3.0.html index 82819e05..5f51f1e6 100644 --- a/release/migration_guide_from_2.x_to_3.0.html +++ b/release/migration_guide_from_2.x_to_3.0.html @@ -6,7 +6,7 @@ <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.19: https://docutils.sourceforge.io/" /> - <title>Preparing for the 3.0 release — NetworkX 3.0rc2.dev0 documentation</title> + <title>Migration guide from 2.X to 3.0 — NetworkX 3.0rc2.dev0 documentation</title> @@ -461,8 +461,8 @@ <article class="bd-article" role="main"> - <section id="preparing-for-the-3-0-release"> -<h1>Preparing for the 3.0 release<a class="headerlink" href="#preparing-for-the-3-0-release" title="Permalink to this heading">#</a></h1> + <section id="migration-guide-from-2-x-to-3-0"> +<h1>Migration guide from 2.X to 3.0<a class="headerlink" href="#migration-guide-from-2-x-to-3-0" title="Permalink to this heading">#</a></h1> <div class="admonition note"> <p class="admonition-title">Note</p> <p>Much of the work leading to the NetworkX 3.0 release will be included @@ -471,7 +471,7 @@ of old code in these releases. This guide will discuss this ongoing work and will help you understand what changes you can make now to minimize the disruption caused by the move to 3.0.</p> </div> -<p>This is a guide for people moving from NetworkX 2.X to NetworkX 3.0</p> +<p>This is a guide for people moving from NetworkX 2.X to NetworkX 3.0.</p> <p>Any issues with these can be discussed on the <a class="reference external" href="https://groups.google.com/forum/#!forum/networkx-discuss">mailing list</a>.</p> <p>The focus of 3.0 release is on addressing years of technical debt, modernizing our codebase, improving performance, and making it easier to contribute. @@ -623,10 +623,7 @@ improving supported for array representations of multi-attribute adjacency:</p> </section> <section id="deprecated-code"> <h2>Deprecated code<a class="headerlink" href="#deprecated-code" title="Permalink to this heading">#</a></h2> -<p>The 2.6 release deprecates over 30 functions. -See <a class="reference internal" href="release_2.6.html#networkx-2-6"><span class="std std-ref">NetworkX 2.6</span></a>.</p> -<p>—</p> -<p>The functions <code class="xref py py-obj docutils literal notranslate"><span class="pre">read_gpickle</span></code> and <code class="xref py py-obj docutils literal notranslate"><span class="pre">write_gpickle</span></code> will be removed in 3.0. +<p>The functions <code class="xref py py-obj docutils literal notranslate"><span class="pre">read_gpickle</span></code> and <code class="xref py py-obj docutils literal notranslate"><span class="pre">write_gpickle</span></code> were removed in 3.0. You can read and write NetworkX graphs as Python pickles.</p> <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">pickle</span> <span class="gp">>>> </span><span class="n">G</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">path_graph</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span> @@ -638,7 +635,7 @@ You can read and write NetworkX graphs as Python pickles.</p> <span class="gp">...</span> </pre></div> </div> -<p>The functions <code class="xref py py-obj docutils literal notranslate"><span class="pre">read_yaml</span></code> and <code class="xref py py-obj docutils literal notranslate"><span class="pre">write_yaml</span></code> will be removed in 3.0. +<p>The functions <code class="xref py py-obj docutils literal notranslate"><span class="pre">read_yaml</span></code> and <code class="xref py py-obj docutils literal notranslate"><span class="pre">write_yaml</span></code> were removed in 3.0. You can read and write NetworkX graphs in YAML format using pyyaml.</p> <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">yaml</span> diff --git a/release/release_2.6.html b/release/release_2.6.html index 36246f37..739b754e 100644 --- a/release/release_2.6.html +++ b/release/release_2.6.html @@ -514,7 +514,7 @@ <p>Supports Python 3.7, 3.8, and 3.9.</p> <p>This release has a larger than normal number of changes in preparation for the upcoming 3.0 release. The current plan is to release 2.7 near the end of summer and 3.0 in late 2021. -See <a class="reference internal" href="migration_guide_from_2.x_to_3.0.html"><span class="doc">Preparing for the 3.0 release</span></a> for more details.</p> +See <a class="reference internal" href="migration_guide_from_2.x_to_3.0.html"><span class="doc">Migration guide from 2.X to 3.0</span></a> for more details.</p> <p>NetworkX is a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.</p> <p>For more information, please visit our <a class="reference external" href="https://networkx.org/">website</a> diff --git a/release/release_dev.html b/release/release_dev.html index 4dc5f857..7782f3eb 100644 --- a/release/release_dev.html +++ b/release/release_dev.html @@ -519,9 +519,9 @@ and our <a class="reference internal" href="../auto_examples/index.html#examples Please send comments and questions to the <a class="reference external" href="http://groups.google.com/group/networkx-discuss">networkx-discuss mailing list</a>.</p> <section id="highlights"> <h2>Highlights<a class="headerlink" href="#highlights" title="Permalink to this heading">#</a></h2> -<p>This release is the result of 4 months of work with over 217 pull requests by -37 contributors. We also have a <span class="xref std std-ref">guide for people moving from NetworkX 2.X -to NetworkX 3.0</span>. Highlights include:</p> +<p>This release is the result of 8 months of work with over 217 pull requests by +37 contributors. We also have a <code class="xref py py-obj docutils literal notranslate"><span class="pre">guide</span> <span class="pre">for</span> <span class="pre">people</span> <span class="pre">moving</span> <span class="pre">from</span> <span class="pre">NetworkX</span> <span class="pre">2.X</span> +<span class="pre">to</span> <span class="pre">NetworkX</span> <span class="pre">3.0</span></code>. Highlights include:</p> <ul class="simple"> <li><p>Better syncing between G._succ and G._adj for directed G. And slightly better speed from all the core adjacency data structures. @@ -545,8 +545,7 @@ then <code class="xref py py-obj docutils literal notranslate"><span class="pre" <li><p>We have added an experimental plugin feature which let users choose alternate backends like GraphBLAS, CuGraph for computation. This is an opt-in feature and may change in future releases.</p></li> -<li><p>Improved integration with the general Scientific Python ecosystem: <link to -the migration guide section?></p></li> +<li><p>Improved integration with the general <code class="xref py py-obj docutils literal notranslate"><span class="pre">Scientific</span> <span class="pre">Python</span> <span class="pre">ecosystem</span></code></p></li> </ul> </section> <section id="improvements"> diff --git a/searchindex.js b/searchindex.js index 35ff31c8..fde3e0eb 100644 --- a/searchindex.js +++ b/searchindex.js @@ -1 +1 @@ -Search.setIndex({"docnames": ["auto_examples/3d_drawing/index", "auto_examples/3d_drawing/mayavi2_spring", "auto_examples/3d_drawing/plot_basic", "auto_examples/3d_drawing/sg_execution_times", "auto_examples/algorithms/index", "auto_examples/algorithms/plot_beam_search", "auto_examples/algorithms/plot_betweenness_centrality", "auto_examples/algorithms/plot_blockmodel", 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109, 110, 111, 114, 115, 121, 127, 128, 143, 165, 172, 198, 199, 202, 204, 215, 216, 218, 219, 220, 221, 230, 231, 235, 256, 267, 277, 278, 281, 289, 299, 310, 314, 324, 325, 335, 338, 361, 378, 383, 385, 387, 389, 390, 392, 399, 405, 406, 407, 422, 427, 428, 432, 433, 437, 460, 464, 480, 520, 521, 559, 560, 581, 582, 583, 590, 593, 614, 619, 626, 631, 635, 653, 656, 660, 661, 662, 676, 679, 681, 683, 691, 698, 699, 703, 711, 717, 718, 735, 737, 748, 760, 782, 786, 796, 862, 868, 886, 887, 890, 891, 907, 913, 924, 925, 926, 927, 943, 949, 967, 968, 971, 972, 988, 994, 1006, 1007, 1008, 1009, 1037, 1039, 1040, 1042, 1043, 1071, 1094, 1100, 1116, 1119, 1120, 1123, 1130, 1131, 1132, 1133, 1135, 1137, 1138, 1139, 1140, 1141, 1142, 1143, 1185, 1192, 1193, 1206, 1214, 1217, 1218, 1219, 1272, 1287, 1288, 1295, 1296, 1297, 1323, 1326, 1328, 1337, 1345, 1348, 1349, 1350, 1390, 1394, 1395, 1397, 1398, 1399, 1401, 1404, 1405, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426], "express": [8, 92, 110, 184, 315, 329, 330, 383, 384, 618, 619, 873, 916, 955, 998, 1199, 1287, 1326], "than": [8, 11, 34, 43, 55, 97, 99, 101, 102, 103, 115, 128, 142, 143, 144, 161, 199, 214, 215, 216, 218, 219, 221, 227, 231, 235, 241, 256, 277, 278, 281, 288, 289, 297, 298, 299, 304, 306, 307, 310, 311, 315, 316, 321, 324, 325, 326, 328, 329, 330, 341, 352, 358, 361, 374, 380, 381, 383, 384, 385, 387, 389, 390, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 425, 426, 429, 435, 464, 468, 469, 500, 527, 537, 559, 560, 581, 582, 583, 590, 625, 626, 635, 636, 652, 653, 656, 658, 659, 673, 676, 678, 679, 686, 690, 692, 693, 694, 698, 699, 711, 731, 735, 737, 748, 752, 761, 786, 887, 925, 947, 968, 992, 1007, 1038, 1042, 1043, 1060, 1102, 1135, 1154, 1162, 1165, 1167, 1172, 1174, 1185, 1187, 1194, 1198, 1226, 1230, 1231, 1236, 1237, 1238, 1239, 1275, 1276, 1296, 1297, 1326, 1328, 1345, 1348, 1349, 1350, 1353, 1354, 1358, 1365, 1366, 1379, 1382, 1395, 1402, 1404, 1405, 1408, 1413, 1423, 1425], "worst": [8, 210, 211, 212, 221, 228, 235, 264, 293, 294, 338, 345, 346, 347, 440, 513, 515, 516, 517, 518], "reus": [8, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 1131, 1132, 1138, 1139, 1140, 1141, 1142, 1328, 1402], "subcircuit": 8, "multipl": [8, 11, 25, 40, 45, 77, 93, 94, 99, 103, 107, 109, 143, 157, 158, 166, 175, 188, 195, 207, 287, 311, 357, 385, 386, 423, 443, 447, 458, 460, 464, 485, 486, 487, 594, 595, 597, 615, 616, 641, 643, 678, 690, 691, 697, 705, 738, 762, 786, 796, 856, 857, 863, 869, 877, 884, 892, 901, 902, 908, 923, 928, 937, 938, 944, 946, 950, 959, 960, 962, 963, 965, 973, 982, 983, 989, 991, 1002, 1003, 1005, 1010, 1037, 1039, 1040, 1045, 1046, 1102, 1103, 1105, 1127, 1135, 1137, 1216, 1217, 1219, 1285, 1291, 1296, 1298, 1326, 1352, 1378, 1393, 1405, 1406, 1412, 1413, 1417, 1425, 1426], "wherea": [8, 103, 682, 762, 786, 791, 1165, 1417], "cannot": [8, 101, 103, 127, 132, 199, 232, 300, 362, 394, 476, 581, 582, 583, 584, 632, 722, 887, 925, 934, 968, 979, 1007, 1043, 1165, 1208, 1209, 1296, 1298, 1302, 1303, 1326, 1345, 1347, 1348, 1349, 1350], "subformula": 8, "onc": [8, 38, 54, 55, 88, 93, 94, 99, 100, 112, 127, 199, 227, 230, 231, 232, 246, 247, 360, 374, 380, 388, 422, 423, 428, 488, 491, 492, 581, 582, 583, 652, 678, 679, 717, 718, 887, 925, 968, 1007, 1046, 1066, 1087, 1217, 1311, 1326, 1403, 1407], "thu": [8, 88, 101, 103, 115, 215, 216, 220, 256, 258, 331, 418, 419, 427, 428, 462, 477, 500, 512, 583, 679, 698, 699, 760, 762, 796, 1037, 1039, 1040, 1043, 1087, 1112, 1148, 1215, 1217, 1234, 1278, 1279, 1296, 1328, 1402, 1405, 1407], "wai": [8, 27, 52, 53, 55, 75, 86, 88, 93, 97, 99, 100, 101, 102, 103, 104, 107, 110, 115, 132, 152, 157, 158, 165, 184, 226, 281, 297, 298, 315, 330, 337, 356, 588, 598, 615, 618, 678, 691, 730, 760, 791, 796, 854, 856, 857, 862, 873, 899, 901, 902, 907, 915, 916, 935, 937, 938, 943, 955, 980, 982, 983, 988, 996, 998, 1037, 1039, 1040, 1041, 1097, 1165, 1213, 1215, 1217, 1239, 1262, 1269, 1272, 1326, 1328, 1330, 1393, 1394, 1404, 1406, 1411, 1426], "infeas": [8, 422], "circuit_to_formula": 8, "dag_to_branch": [8, 758, 1408], "transfer": [8, 202, 204, 230, 231, 469, 890, 891, 926, 927, 971, 972, 1008, 1009, 1420], "oper": [8, 30, 52, 95, 101, 112, 115, 168, 184, 189, 227, 374, 423, 460, 546, 547, 548, 552, 553, 554, 577, 595, 598, 601, 671, 672, 673, 674, 679, 680, 758, 786, 865, 873, 878, 910, 916, 946, 955, 960, 991, 998, 1036, 1068, 1088, 1103, 1164, 1218, 1219, 1295, 1302, 1319, 1323, 1325, 1326, 1393, 1394, 1400, 1404, 1405, 1406, 1407, 1408, 1411, 1412, 1413, 1414, 1417], "variabl": [8, 94, 132, 373, 530, 540, 618, 619, 732, 796, 1037, 1038, 1039, 1040, 1154, 1165, 1326, 1408, 1412, 1413, 1414, 1420], "formula_to_str": 8, "_to_str": 8, "root": [8, 67, 84, 293, 294, 338, 387, 389, 390, 394, 449, 460, 559, 577, 609, 671, 673, 678, 704, 728, 730, 739, 760, 791, 1119, 1120, 1125, 1126, 1145, 1147, 1235, 1271, 1272, 1323, 1365, 1366, 1393, 1406, 1407, 1408, 1412, 1413, 1423, 1425], "children": [8, 460, 577, 1145, 1155, 1272, 1365, 1366], "otherwis": [8, 92, 110, 146, 149, 171, 178, 184, 185, 198, 217, 230, 249, 250, 284, 297, 298, 303, 306, 307, 311, 315, 316, 322, 323, 324, 325, 326, 329, 330, 343, 353, 358, 393, 394, 395, 396, 397, 398, 410, 411, 412, 418, 419, 422, 425, 426, 462, 463, 464, 470, 479, 488, 490, 494, 495, 496, 498, 499, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 521, 555, 562, 563, 568, 572, 574, 584, 586, 588, 597, 601, 616, 618, 619, 633, 663, 673, 687, 688, 689, 696, 698, 699, 734, 735, 736, 737, 751, 848, 867, 873, 874, 886, 893, 912, 916, 917, 924, 929, 934, 948, 955, 956, 967, 974, 979, 993, 998, 999, 1006, 1068, 1091, 1135, 1137, 1165, 1185, 1197, 1217, 1270, 1282, 1283, 1284, 1307, 1309, 1312, 1342, 1356, 1357, 1376, 1409, 1413, 1426], "child": [8, 1147, 1272], "must": [8, 11, 93, 94, 95, 99, 100, 103, 110, 151, 152, 158, 161, 171, 204, 206, 207, 214, 215, 216, 219, 230, 231, 232, 252, 253, 257, 258, 259, 260, 261, 262, 264, 267, 268, 269, 271, 273, 276, 281, 285, 297, 298, 306, 307, 315, 316, 317, 318, 319, 324, 325, 327, 329, 330, 342, 361, 362, 363, 378, 382, 385, 391, 410, 411, 412, 413, 425, 429, 440, 471, 472, 473, 474, 475, 545, 546, 547, 548, 549, 550, 551, 553, 555, 556, 557, 558, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 577, 578, 579, 580, 584, 585, 586, 587, 588, 589, 593, 597, 599, 601, 602, 603, 604, 615, 626, 627, 632, 633, 635, 636, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 671, 672, 673, 674, 680, 690, 692, 698, 699, 707, 721, 734, 735, 736, 737, 789, 796, 853, 854, 857, 867, 891, 892, 898, 899, 902, 912, 928, 934, 938, 972, 973, 979, 983, 1010, 1037, 1038, 1039, 1040, 1063, 1071, 1085, 1102, 1133, 1137, 1162, 1165, 1173, 1176, 1186, 1188, 1190, 1193, 1197, 1199, 1209, 1213, 1217, 1219, 1235, 1239, 1240, 1270, 1275, 1276, 1277, 1278, 1279, 1295, 1296, 1298, 1307, 1309, 1310, 1311, 1312, 1315, 1333, 1337, 1338, 1339, 1340, 1359, 1361, 1362, 1363, 1364, 1365, 1366, 1376, 1393, 1394, 1395, 1407, 1426], "NOT": [8, 110, 199, 549, 550, 551, 748, 887, 925, 968, 1007], "util": [8, 14, 36, 44, 45, 93, 97, 102, 103, 229, 230, 231, 316, 374, 423, 425, 426, 429, 460, 496, 678, 679, 758, 1044, 1242, 1299, 1301, 1303, 1310, 1319, 1320, 1321, 1325, 1402, 1406, 1407, 1411, 1413, 1416, 1419], "arbitrary_el": [8, 1392, 1413], "nb": [8, 1331, 1334], "left": [8, 71, 115, 183, 311, 312, 322, 324, 325, 385, 559, 560, 584, 616, 688, 689, 739, 1106, 1134, 1136, 1146, 1179, 1206, 1280, 1355, 1358, 1404], "right": [8, 71, 110, 111, 115, 152, 206, 322, 385, 427, 428, 500, 559, 560, 584, 585, 587, 588, 615, 616, 688, 689, 739, 854, 935, 980, 1134, 1136, 1146, 1155, 1157, 1179, 1206, 1213, 1215, 1270, 1280], "littl": [8, 94, 298, 307], "mislead": 8, "That": [8, 97, 132, 165, 212, 221, 227, 295, 385, 436, 465, 525, 535, 555, 588, 657, 671, 672, 673, 674, 691, 704, 717, 791, 862, 907, 943, 988, 1046, 1162, 1210, 1296, 1388, 1404, 1409], "okai": 8, "becaus": [8, 11, 54, 69, 94, 99, 101, 102, 103, 112, 132, 161, 215, 216, 220, 255, 311, 378, 387, 389, 390, 394, 411, 412, 427, 494, 498, 499, 500, 510, 569, 585, 587, 615, 616, 632, 652, 934, 979, 1038, 1236, 1273, 1296, 1303, 1326, 1345, 1350, 1404, 1407, 1416], "AND": [8, 110, 598, 748, 762], "OR": [8, 110, 157, 175, 188, 856, 869, 877, 901, 937, 947, 950, 959, 982, 992], "symmetr": [8, 145, 148, 237, 545, 586, 593, 761, 1173, 1192, 1235, 1246, 1250, 1251, 1256, 1258, 1269, 1320, 1321, 1387], "It": [8, 52, 56, 58, 92, 93, 94, 97, 99, 101, 102, 104, 107, 110, 112, 115, 132, 172, 184, 207, 214, 215, 216, 229, 230, 231, 249, 260, 261, 262, 264, 278, 310, 316, 324, 325, 326, 343, 346, 347, 351, 353, 412, 414, 415, 416, 417, 418, 419, 429, 438, 440, 452, 457, 464, 480, 496, 500, 508, 530, 540, 545, 559, 560, 565, 566, 567, 582, 588, 594, 595, 598, 600, 601, 615, 619, 628, 629, 630, 652, 658, 659, 663, 671, 674, 692, 717, 718, 719, 760, 761, 762, 791, 796, 868, 873, 892, 913, 916, 928, 949, 955, 973, 994, 998, 1010, 1012, 1013, 1018, 1037, 1038, 1039, 1040, 1054, 1117, 1170, 1174, 1200, 1201, 1206, 1207, 1210, 1217, 1223, 1227, 1234, 1243, 1244, 1245, 1246, 1247, 1248, 1249, 1250, 1251, 1253, 1254, 1258, 1261, 1263, 1264, 1269, 1275, 1276, 1277, 1280, 1296, 1297, 1323, 1324, 1326, 1328, 1343, 1382, 1383, 1393, 1395, 1398, 1402, 1404, 1407, 1408, 1409, 1411, 1412, 1413, 1426], "just": [8, 99, 102, 103, 104, 184, 199, 338, 374, 439, 464, 559, 560, 577, 660, 661, 662, 692, 791, 873, 887, 916, 925, 946, 955, 960, 968, 991, 998, 1007, 1120, 1126, 1229, 1278, 1279, 1296, 1328, 1393, 1404, 1406], "operand": 8, "predict": [8, 567, 568, 569, 570, 571, 572, 573, 574, 591, 592, 758, 1325, 1402, 1406, 1412], "henc": [8, 168, 189, 521, 865, 878, 910, 946, 960, 991, 1059, 1202, 1383], "doe": [8, 77, 93, 94, 99, 101, 102, 103, 104, 114, 115, 132, 147, 153, 154, 165, 168, 189, 207, 208, 227, 228, 229, 230, 231, 232, 293, 308, 339, 340, 342, 343, 352, 357, 373, 382, 385, 410, 414, 426, 450, 469, 494, 495, 496, 497, 498, 499, 500, 502, 503, 506, 507, 509, 510, 511, 512, 534, 544, 549, 550, 551, 564, 566, 583, 584, 586, 589, 601, 612, 626, 627, 678, 691, 693, 694, 698, 699, 717, 718, 721, 722, 723, 724, 725, 726, 762, 862, 865, 878, 892, 907, 910, 928, 943, 946, 960, 973, 988, 991, 1010, 1038, 1043, 1066, 1070, 1072, 1081, 1102, 1103, 1105, 1106, 1107, 1109, 1114, 1173, 1175, 1177, 1192, 1207, 1222, 1223, 1227, 1229, 1234, 1241, 1296, 1300, 1303, 1326, 1333, 1334, 1341, 1342, 1344, 1351, 1353, 1354, 1355, 1356, 1357, 1358, 1371, 1379, 1380, 1381, 1383, 1393, 1404, 1405, 1406, 1410, 1417, 1426], "necessarili": [8, 99, 341, 451, 483, 559, 560, 641, 643, 1038, 1219], "behav": [8, 88, 103, 159, 190, 200, 220, 351, 858, 879, 888, 903, 939, 969, 984, 1229, 1296, 1395, 1404], "everi": [8, 11, 57, 88, 93, 109, 112, 120, 144, 157, 161, 177, 211, 212, 220, 221, 229, 230, 231, 235, 243, 264, 287, 295, 300, 324, 325, 343, 352, 380, 397, 437, 439, 440, 450, 462, 471, 472, 473, 474, 475, 477, 483, 484, 491, 512, 516, 565, 606, 614, 615, 619, 632, 633, 635, 636, 663, 685, 687, 688, 717, 718, 791, 856, 901, 937, 982, 1052, 1053, 1054, 1070, 1071, 1072, 1085, 1086, 1102, 1103, 1105, 1106, 1107, 1108, 1109, 1110, 1111, 1114, 1115, 1116, 1117, 1148, 1162, 1195, 1216, 1217, 1257, 1264, 1278, 1279, 1296, 1407], "left_subformula": 8, "right_subformula": 8, "in_degre": [8, 166, 188, 491, 678, 863, 877, 944, 959, 1177, 1207, 1208, 1404, 1406, 1407, 1426], "ha": [8, 11, 16, 44, 67, 88, 91, 93, 94, 95, 97, 99, 100, 101, 102, 103, 105, 107, 110, 112, 116, 120, 127, 152, 161, 165, 166, 173, 174, 175, 184, 188, 198, 207, 212, 214, 215, 219, 220, 226, 227, 229, 230, 231, 232, 235, 238, 239, 240, 241, 242, 243, 244, 247, 249, 252, 269, 271, 272, 273, 274, 275, 276, 282, 289, 291, 293, 294, 295, 300, 305, 310, 324, 331, 343, 352, 355, 356, 363, 364, 365, 373, 378, 380, 381, 383, 384, 385, 386, 391, 393, 394, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 424, 427, 428, 429, 439, 450, 458, 460, 466, 467, 468, 471, 472, 473, 474, 475, 476, 477, 480, 491, 492, 493, 494, 495, 496, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 522, 564, 566, 577, 578, 581, 590, 593, 605, 607, 610, 611, 622, 623, 624, 628, 629, 630, 632, 633, 634, 635, 636, 638, 646, 647, 649, 652, 657, 658, 682, 688, 690, 692, 697, 711, 717, 718, 729, 730, 731, 739, 749, 786, 791, 854, 862, 863, 869, 873, 877, 886, 892, 899, 907, 908, 916, 924, 928, 935, 943, 944, 948, 950, 955, 959, 967, 973, 980, 988, 989, 993, 998, 1006, 1010, 1040, 1043, 1045, 1066, 1068, 1070, 1072, 1075, 1080, 1084, 1098, 1099, 1101, 1102, 1103, 1105, 1122, 1130, 1145, 1154, 1160, 1162, 1165, 1176, 1180, 1185, 1193, 1195, 1196, 1197, 1198, 1199, 1207, 1210, 1211, 1215, 1217, 1222, 1234, 1239, 1243, 1244, 1248, 1249, 1254, 1259, 1261, 1264, 1267, 1269, 1270, 1272, 1275, 1276, 1277, 1278, 1279, 1281, 1282, 1283, 1284, 1285, 1286, 1289, 1291, 1293, 1296, 1300, 1326, 1328, 1330, 1333, 1334, 1353, 1354, 1371, 1372, 1379, 1382, 1393, 1394, 1395, 1398, 1403, 1404, 1405, 1406, 1407, 1409, 1413, 1414, 1416, 1423, 1425], "output": [8, 13, 16, 89, 93, 101, 102, 103, 109, 197, 287, 288, 345, 374, 380, 494, 498, 499, 509, 510, 575, 588, 677, 678, 691, 722, 1045, 1193, 1197, 1199, 1269, 1296, 1326, 1334, 1341, 1344, 1355, 1358, 1399, 1402, 1404, 1406, 1411, 1413, 1414, 1426], "two": [8, 11, 16, 27, 34, 38, 43, 54, 55, 57, 58, 65, 67, 71, 88, 93, 95, 99, 100, 102, 109, 112, 114, 115, 120, 132, 151, 171, 175, 184, 185, 188, 202, 207, 211, 212, 213, 214, 215, 216, 217, 220, 221, 226, 227, 230, 231, 232, 245, 249, 251, 252, 253, 257, 258, 260, 261, 262, 265, 269, 270, 271, 272, 273, 274, 275, 276, 282, 285, 286, 287, 289, 305, 311, 315, 316, 322, 326, 329, 330, 337, 341, 343, 345, 351, 352, 358, 359, 377, 380, 381, 383, 391, 411, 412, 419, 423, 428, 429, 430, 431, 442, 443, 444, 445, 447, 452, 453, 454, 457, 462, 471, 472, 473, 474, 475, 476, 480, 491, 494, 498, 499, 500, 502, 503, 506, 508, 509, 510, 511, 521, 545, 549, 550, 551, 555, 559, 560, 561, 562, 563, 564, 565, 566, 568, 569, 572, 574, 578, 584, 585, 586, 587, 588, 593, 598, 605, 607, 608, 610, 611, 615, 619, 626, 627, 629, 632, 633, 635, 636, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 671, 672, 673, 674, 675, 676, 680, 692, 694, 731, 732, 738, 739, 760, 761, 762, 780, 786, 791, 796, 853, 867, 869, 873, 874, 877, 890, 892, 898, 912, 916, 917, 926, 928, 934, 946, 948, 950, 955, 956, 959, 960, 971, 973, 979, 991, 993, 998, 999, 1008, 1010, 1019, 1020, 1021, 1022, 1036, 1037, 1039, 1040, 1056, 1084, 1088, 1098, 1100, 1101, 1106, 1107, 1108, 1109, 1114, 1116, 1134, 1146, 1147, 1149, 1151, 1152, 1156, 1174, 1185, 1186, 1193, 1194, 1195, 1196, 1197, 1198, 1199, 1204, 1207, 1210, 1211, 1215, 1217, 1218, 1243, 1244, 1253, 1271, 1272, 1275, 1276, 1294, 1295, 1296, 1323, 1324, 1326, 1328, 1359, 1360, 1363, 1393, 1394, 1395, 1397, 1402, 1404, 1405, 1406, 1407, 1410, 1411, 1413, 1425], "layer": [8, 36, 55, 61, 67, 103, 438, 705, 1038, 1109, 1420], "third": [8, 102, 114, 249, 422, 467, 585, 587, 734, 736, 1217, 1226, 1262, 1263, 1326, 1407], "appear": [8, 83, 93, 95, 99, 100, 102, 179, 204, 230, 231, 238, 243, 246, 247, 277, 363, 364, 365, 378, 451, 452, 453, 455, 466, 470, 584, 585, 587, 588, 675, 679, 707, 730, 734, 736, 891, 972, 1036, 1088, 1102, 1136, 1150, 1152, 1154, 1157, 1159, 1187, 1188, 1277, 1282, 1323, 1324, 1345, 1348, 1349, 1350, 1382, 1407, 1413, 1414], "both": [8, 52, 55, 92, 93, 94, 100, 101, 102, 103, 115, 161, 164, 204, 214, 215, 216, 217, 240, 257, 258, 259, 264, 282, 286, 287, 289, 337, 358, 379, 383, 415, 417, 418, 419, 423, 427, 440, 470, 502, 506, 545, 575, 581, 598, 600, 601, 602, 603, 604, 605, 606, 607, 610, 611, 615, 621, 635, 636, 653, 654, 655, 676, 711, 720, 760, 761, 762, 782, 891, 972, 1020, 1036, 1066, 1075, 1080, 1084, 1088, 1097, 1120, 1126, 1144, 1165, 1189, 1192, 1199, 1207, 1210, 1211, 1213, 1215, 1282, 1296, 1326, 1328, 1358, 1363, 1364, 1387, 1393, 1395, 1402, 1413, 1416, 1417, 1425, 1426], "negat": 8, "sole": [8, 786, 1278, 1279, 1326], "fourth": [8, 230, 231, 1326, 1404], "digraph": [8, 10, 11, 16, 21, 25, 41, 45, 56, 61, 67, 69, 70, 82, 88, 101, 102, 115, 132, 151, 152, 156, 157, 158, 160, 162, 163, 165, 166, 168, 170, 171, 172, 175, 176, 185, 186, 187, 188, 189, 192, 193, 194, 195, 196, 198, 199, 202, 204, 207, 208, 216, 227, 229, 230, 231, 240, 246, 247, 299, 308, 314, 318, 319, 321, 327, 328, 334, 335, 336, 337, 339, 340, 342, 343, 388, 391, 393, 396, 397, 398, 399, 401, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 430, 431, 437, 450, 452, 453, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 481, 482, 492, 494, 495, 496, 497, 498, 499, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 513, 514, 518, 519, 523, 555, 566, 575, 576, 577, 588, 590, 613, 615, 623, 630, 636, 643, 644, 652, 656, 657, 658, 659, 663, 678, 688, 690, 693, 696, 697, 698, 699, 700, 701, 702, 706, 707, 708, 709, 711, 716, 717, 718, 719, 721, 722, 723, 724, 725, 726, 740, 741, 744, 745, 746, 747, 748, 749, 750, 752, 760, 789, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 904, 905, 906, 907, 908, 911, 912, 913, 915, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 935, 936, 937, 938, 940, 941, 942, 943, 949, 957, 958, 963, 964, 965, 966, 967, 968, 972, 973, 974, 975, 977, 978, 980, 981, 982, 983, 985, 986, 987, 988, 989, 994, 996, 1000, 1001, 1003, 1004, 1005, 1006, 1007, 1010, 1035, 1037, 1038, 1039, 1040, 1041, 1052, 1062, 1066, 1070, 1072, 1075, 1080, 1083, 1084, 1098, 1099, 1101, 1118, 1135, 1150, 1154, 1168, 1169, 1170, 1173, 1177, 1178, 1180, 1182, 1183, 1184, 1185, 1189, 1217, 1270, 1272, 1273, 1274, 1283, 1284, 1287, 1290, 1292, 1298, 1323, 1326, 1333, 1337, 1342, 1356, 1357, 1362, 1365, 1366, 1371, 1393, 1399, 1401, 1402, 1404, 1405, 1406, 1407, 1408, 1409, 1411, 1412, 1413, 1414, 1416, 1417, 1424, 1425, 1426], "add_nod": [8, 11, 26, 34, 69, 74, 89, 102, 157, 184, 246, 339, 340, 398, 422, 491, 492, 496, 504, 505, 508, 522, 523, 605, 607, 610, 611, 691, 796, 856, 873, 901, 916, 937, 955, 982, 998, 1037, 1039, 1040, 1086, 1275, 1326, 1345, 1407, 1408, 1417, 1426], "get_node_attribut": [8, 39, 44, 71, 1213, 1404], "600": [8, 10, 12], "font_siz": [8, 16, 21, 25, 32, 35, 38, 45, 46, 1133, 1134, 1136], "22": [8, 35, 64, 66, 383, 384, 1271, 1323, 1403, 1408, 1412, 1422], "multipartite_layout": [8, 36, 61, 67, 1412, 1414, 1420], "subset_kei": [8, 36, 61, 67, 1109], "equal": [8, 36, 81, 144, 214, 215, 216, 230, 231, 238, 269, 271, 273, 276, 288, 297, 298, 300, 303, 306, 307, 310, 311, 312, 315, 316, 320, 323, 324, 325, 329, 330, 331, 373, 410, 411, 412, 413, 418, 419, 428, 471, 474, 476, 491, 494, 495, 496, 498, 499, 502, 503, 504, 505, 506, 507, 508, 509, 510, 525, 535, 545, 552, 553, 554, 555, 568, 572, 605, 623, 657, 671, 672, 673, 674, 687, 688, 689, 690, 721, 722, 740, 741, 753, 761, 791, 1112, 1116, 1162, 1165, 1198, 1204, 1230, 1239, 1271, 1280, 1291, 1307, 1309, 1312, 1398, 1399], "106": [8, 17, 39, 47], "plot_circuit": [8, 17], "southern": [9, 1265], "women": [9, 1265, 1398, 1406], "unipartit": [9, 115, 258, 259, 358], "properti": [9, 11, 18, 22, 33, 63, 86, 101, 102, 103, 112, 134, 159, 161, 166, 168, 175, 176, 179, 184, 188, 189, 190, 200, 284, 285, 286, 287, 288, 363, 364, 365, 388, 476, 500, 545, 569, 619, 685, 858, 863, 865, 869, 870, 873, 877, 878, 879, 888, 903, 908, 910, 916, 939, 944, 946, 950, 951, 955, 959, 960, 969, 984, 989, 991, 998, 1085, 1086, 1122, 1134, 1136, 1193, 1202, 1217, 1219, 1269, 1283, 1284, 1326, 1328, 1383, 1398, 1405, 1406, 1407, 1408, 1413, 1417, 1426], "These": [9, 52, 58, 73, 79, 86, 93, 94, 105, 112, 336, 385, 494, 512, 559, 671, 673, 732, 748, 779, 786, 1038, 1045, 1047, 1323, 1326, 1385, 1387, 1392, 1394, 1395, 1397, 1399, 1404, 1405, 1411, 1426], "were": [9, 65, 88, 99, 101, 104, 215, 216, 220, 289, 305, 410, 437, 460, 588, 962, 1002, 1199, 1393, 1395, 1399, 1402, 1405, 1406, 1407, 1413, 1416], "et": [9, 210, 226, 227, 315, 316, 322, 330, 334, 337, 345, 352, 358, 373, 380, 381, 423, 425, 426, 451, 569, 591, 592, 681, 682, 684, 693, 1202], "al": [9, 210, 226, 227, 315, 316, 322, 330, 334, 337, 345, 352, 358, 373, 380, 381, 423, 425, 426, 451, 569, 591, 592, 681, 682, 684, 693, 1202, 1407, 1413], "1930": [9, 1396], "thei": [9, 54, 58, 65, 71, 92, 93, 94, 97, 99, 100, 101, 102, 103, 104, 105, 107, 112, 132, 151, 165, 207, 213, 220, 249, 285, 287, 288, 296, 297, 298, 301, 302, 306, 307, 308, 309, 351, 362, 374, 391, 396, 427, 451, 452, 453, 454, 464, 465, 471, 472, 473, 474, 475, 496, 504, 505, 508, 512, 546, 547, 548, 559, 560, 576, 583, 586, 588, 600, 604, 675, 676, 704, 717, 750, 760, 786, 853, 862, 892, 898, 907, 928, 934, 943, 962, 973, 979, 988, 1002, 1010, 1036, 1038, 1066, 1085, 1088, 1109, 1120, 1126, 1133, 1135, 1137, 1151, 1159, 1165, 1193, 1197, 1198, 1217, 1271, 1272, 1323, 1328, 1353, 1354, 1356, 1357, 1359, 1363, 1394, 1396, 1402, 1404, 1406, 1409, 1414, 1426], "repres": [9, 11, 26, 43, 52, 54, 57, 67, 92, 99, 107, 115, 230, 231, 265, 281, 283, 286, 287, 288, 291, 292, 338, 350, 361, 362, 363, 377, 378, 380, 381, 382, 385, 386, 391, 448, 452, 453, 455, 457, 460, 465, 466, 494, 495, 498, 499, 500, 502, 503, 506, 507, 509, 510, 521, 565, 577, 578, 579, 580, 586, 588, 609, 615, 618, 619, 656, 660, 664, 667, 676, 679, 691, 692, 695, 697, 698, 699, 700, 702, 728, 730, 731, 734, 736, 739, 752, 786, 791, 796, 1019, 1020, 1021, 1022, 1037, 1038, 1039, 1040, 1045, 1081, 1102, 1140, 1151, 1185, 1193, 1194, 1196, 1197, 1198, 1199, 1209, 1217, 1240, 1243, 1246, 1250, 1258, 1267, 1269, 1272, 1273, 1278, 1279, 1323, 1324, 1326, 1329, 1330, 1346, 1347, 1388, 1393, 1406], "observ": [9, 13, 132, 223, 1414, 1426], "attend": 9, "14": [9, 11, 16, 19, 25, 38, 44, 64, 66, 71, 96, 110, 229, 230, 231, 383, 384, 405, 406, 501, 619, 690, 1150, 1242, 1250, 1262, 1325, 1406, 1408, 1426], "event": [9, 25, 99, 100, 110, 1165, 1229, 1300], "18": [9, 44, 64, 66, 93, 324, 325, 345, 383, 384, 618, 1169, 1249, 1255, 1258, 1260, 1263, 1269, 1393, 1406, 1416, 1417, 1421, 1426], "bipartit": [9, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 350, 351, 358, 377, 439, 440, 443, 581, 588, 758, 1043, 1106, 1151, 1203, 1204, 1205, 1265, 1325, 1395, 1398, 1399, 1400, 1401, 1406, 1407, 1411, 1413, 1417, 1421, 1425], "biadjac": [9, 282, 283, 1400, 1406], "7": [9, 12, 14, 19, 25, 35, 44, 46, 63, 64, 65, 66, 68, 89, 99, 101, 102, 115, 125, 151, 158, 170, 171, 192, 207, 232, 268, 297, 299, 314, 322, 327, 332, 333, 339, 340, 342, 362, 374, 380, 391, 403, 410, 413, 414, 415, 423, 424, 425, 426, 441, 445, 446, 483, 496, 501, 508, 511, 512, 555, 581, 586, 618, 619, 630, 652, 658, 663, 671, 674, 680, 695, 703, 706, 707, 708, 730, 747, 750, 761, 796, 853, 857, 866, 867, 881, 892, 898, 902, 911, 912, 915, 920, 928, 934, 938, 947, 973, 979, 983, 992, 996, 1010, 1037, 1039, 1040, 1052, 1053, 1085, 1100, 1104, 1148, 1212, 1242, 1248, 1250, 1251, 1255, 1258, 1260, 1273, 1323, 1326, 1330, 1339, 1340, 1345, 1348, 1349, 1350, 1382, 1392, 1394, 1402, 1403, 1405, 1408, 1409, 1410, 1411, 1412, 1413, 1426], "12": [9, 11, 19, 25, 44, 50, 55, 58, 64, 65, 66, 89, 91, 93, 229, 230, 231, 265, 345, 380, 381, 392, 399, 405, 406, 407, 449, 486, 501, 516, 568, 572, 574, 606, 616, 1052, 1053, 1054, 1133, 1136, 1150, 1244, 1245, 1249, 1254, 1257, 1263, 1335, 1406, 1408, 1412, 1426], "9": [9, 11, 12, 19, 25, 35, 44, 46, 63, 64, 65, 66, 68, 82, 89, 101, 102, 111, 115, 125, 232, 293, 295, 339, 340, 342, 346, 347, 356, 374, 380, 405, 406, 424, 438, 449, 494, 496, 501, 504, 505, 508, 545, 566, 581, 586, 676, 706, 707, 708, 761, 1100, 1104, 1148, 1150, 1194, 1199, 1212, 1217, 1235, 1246, 1255, 1267, 1273, 1283, 1284, 1323, 1326, 1328, 1396, 1403, 1412, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426], "11": [9, 25, 33, 44, 64, 65, 66, 68, 89, 102, 110, 115, 157, 210, 239, 240, 297, 298, 303, 306, 307, 323, 392, 399, 405, 406, 407, 413, 415, 417, 422, 501, 514, 517, 606, 618, 680, 721, 738, 856, 901, 937, 982, 1052, 1053, 1054, 1100, 1150, 1287, 1403, 1410, 1413, 1414, 1419, 1424, 1425, 1426], "13": [9, 11, 38, 44, 64, 66, 89, 91, 156, 229, 230, 231, 343, 501, 703, 855, 900, 936, 981, 1150, 1192, 1406, 1420, 1426], "16": [9, 19, 31, 44, 45, 59, 64, 66, 70, 229, 230, 231, 346, 347, 387, 389, 390, 394, 453, 508, 511, 512, 519, 571, 592, 606, 748, 749, 750, 1109, 1205, 1256, 1271, 1286, 1323, 1406, 1411, 1426], "17": [9, 21, 44, 64, 66, 103, 229, 230, 231, 297, 508, 680, 693, 1405, 1406, 1426], "friend": [9, 545, 1407, 1412], "member": [9, 92, 93, 94, 100, 112, 315, 317, 318, 319, 330, 391, 483, 484, 586, 691, 1222, 1267, 1403], "evelyn": 9, "jefferson": 9, "laura": 9, "mandevil": 9, "theresa": 9, "anderson": 9, "brenda": 9, "roger": 9, "charlott": 9, "mcdowd": 9, "franc": 9, "eleanor": 9, "nye": 9, "pearl": [9, 132], "oglethorp": 9, "ruth": 9, "desand": 9, "vern": 9, "sanderson": 9, "myra": 9, "liddel": 9, "katherina": 9, "sylvia": 9, "avondal": 9, "nora": 9, "fayett": 9, "helen": 9, "lloyd": 9, "dorothi": 9, "murchison": 9, "olivia": 9, "carleton": 9, "flora": 9, "price": 9, "meet": [9, 94, 1165, 1196, 1197, 1198], "50": [9, 25, 30, 34, 40, 50, 54, 55, 56, 57, 64, 65, 272, 312, 1117, 1193, 1197, 1198, 1251, 1297, 1302], "45": [9, 58, 64, 110, 226, 300, 409, 1175], "57": [9, 64], "46": [9, 64, 235, 564, 619, 1264], "24": [9, 19, 37, 64, 66, 68, 103, 383, 384, 496, 505, 508, 703, 1212, 1229, 1244, 1262, 1271, 1403], "32": [9, 64, 66, 68, 209, 211, 212, 383, 384, 564, 703, 1403, 1411], "36": [9, 21, 64, 68, 752, 1150, 1262, 1271, 1353, 1354, 1379, 1403], "31": [9, 64, 66, 229, 230, 231, 260, 261, 262, 289, 383, 384, 409, 703, 1226, 1235, 1403], "40": [9, 50, 64, 80, 101, 297, 300, 555, 672, 1173, 1240, 1271], "38": [9, 64, 688, 1271], "33": [9, 58, 64, 66, 68, 93, 383, 384, 500, 514, 703, 1267, 1271, 1403, 1414], "37": [9, 56, 64, 68, 303, 311, 312, 323, 324, 325, 496, 508, 1039, 1040, 1271, 1393, 1403, 1408, 1425], "43": [9, 64, 324, 325, 606, 1244, 1271], "34": [9, 64, 68, 331, 508, 762, 1271, 1403], "algorithm": [9, 14, 15, 44, 52, 54, 88, 93, 94, 95, 96, 102, 103, 107, 109, 110, 111, 112, 114, 115, 117, 120, 121, 122, 125, 127, 128, 132, 133, 136, 141, 151, 210, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 226, 227, 228, 229, 230, 231, 232, 235, 249, 251, 252, 253, 254, 255, 256, 258, 260, 261, 262, 263, 264, 265, 266, 267, 272, 275, 277, 278, 280, 282, 284, 285, 286, 287, 288, 289, 290, 293, 296, 297, 298, 299, 301, 302, 303, 306, 307, 308, 309, 311, 312, 315, 320, 322, 323, 324, 325, 326, 329, 330, 331, 332, 333, 337, 339, 340, 341, 342, 343, 345, 346, 347, 352, 358, 361, 362, 366, 371, 372, 373, 374, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 389, 390, 394, 399, 405, 406, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 421, 422, 424, 425, 426, 427, 428, 429, 430, 432, 433, 435, 437, 440, 449, 451, 452, 453, 454, 455, 460, 464, 466, 468, 481, 482, 483, 488, 494, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 512, 513, 514, 516, 519, 520, 521, 527, 537, 546, 547, 548, 552, 553, 554, 555, 556, 557, 558, 564, 566, 569, 577, 581, 582, 583, 589, 591, 592, 593, 600, 614, 616, 618, 619, 624, 625, 626, 627, 628, 629, 630, 632, 633, 635, 636, 639, 652, 653, 657, 658, 659, 660, 663, 664, 667, 671, 672, 673, 674, 676, 677, 678, 680, 681, 682, 683, 686, 690, 691, 692, 693, 695, 696, 697, 698, 699, 700, 701, 702, 711, 717, 721, 722, 729, 731, 732, 734, 735, 736, 737, 738, 749, 764, 765, 768, 770, 775, 776, 780, 786, 789, 790, 791, 853, 898, 934, 979, 1011, 1038, 1042, 1043, 1105, 1106, 1107, 1109, 1114, 1116, 1117, 1125, 1126, 1155, 1165, 1168, 1169, 1177, 1178, 1179, 1180, 1181, 1185, 1186, 1187, 1188, 1193, 1195, 1200, 1201, 1202, 1205, 1207, 1209, 1210, 1216, 1223, 1224, 1226, 1227, 1228, 1230, 1231, 1232, 1234, 1235, 1239, 1260, 1269, 1275, 1276, 1277, 1298, 1302, 1319, 1320, 1321, 1323, 1325, 1328, 1367, 1368, 1386, 1393, 1394, 1395, 1400, 1401, 1402, 1403, 1406, 1407, 1408, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1419, 1422, 1424, 1425, 1426], "davis_southern_women_graph": [9, 88, 263], "top": [9, 34, 52, 67, 106, 111, 112, 115, 125, 260, 272, 284, 350, 381, 670, 675, 770, 1106, 1134, 1136, 1252, 1396, 1399, 1407, 1412, 1413, 1416], "bottom": [9, 91, 115, 260, 272, 274, 284, 285, 286, 287, 288, 350, 381, 1134, 1136, 1155, 1404, 1416], "biadjacency_matrix": [9, 283], "onto": [9, 284, 285, 286, 287, 288, 559, 560], "projected_graph": [9, 115, 284, 285, 286, 288, 351], "keep": [9, 92, 93, 94, 115, 204, 345, 346, 347, 362, 377, 387, 389, 390, 394, 583, 598, 693, 694, 891, 972, 1117, 1207, 1210, 1278, 1279, 1296, 1376, 1394, 1411, 1414], "co": [9, 26, 94, 99, 144, 752, 1326], "occur": [9, 93, 95, 100, 230, 231, 277, 278, 280, 383, 581, 582, 583, 588, 1043, 1117, 1120, 1126, 1282, 1296], "count": [9, 185, 237, 238, 242, 243, 245, 297, 298, 310, 315, 330, 360, 386, 443, 568, 597, 619, 749, 753, 874, 917, 944, 950, 956, 959, 999, 1060, 1179, 1278, 1279, 1406, 1407, 1416], "share": [9, 54, 58, 92, 94, 112, 165, 199, 214, 215, 216, 221, 278, 285, 287, 288, 294, 358, 359, 376, 418, 419, 460, 462, 480, 569, 578, 691, 732, 862, 887, 907, 925, 943, 968, 988, 1007, 1217, 1328], "contact": [9, 92, 688, 1195, 1326], "weighted_projected_graph": [9, 284, 285, 286, 287, 1417], "648": 9, "074": [9, 17], "plot_davis_club": [9, 17], "retain": [10, 102, 110, 230, 284, 285, 286, 287, 288, 1100, 1187, 1295], "pattern": [10, 54, 93, 103, 236, 241, 244, 248, 385, 494, 519, 555, 671, 672, 673, 674, 690, 691, 693, 762, 786, 1036, 1088, 1388, 1413], "add": [10, 11, 26, 34, 41, 45, 49, 52, 61, 71, 88, 89, 91, 93, 94, 101, 102, 105, 106, 115, 151, 152, 153, 154, 156, 157, 158, 164, 207, 222, 223, 229, 282, 285, 341, 374, 411, 412, 423, 428, 430, 431, 450, 460, 581, 582, 583, 589, 614, 615, 618, 619, 654, 690, 701, 717, 718, 796, 850, 853, 854, 855, 856, 857, 892, 895, 898, 899, 900, 901, 902, 928, 931, 934, 935, 936, 937, 938, 973, 976, 979, 980, 981, 982, 983, 984, 1010, 1037, 1038, 1039, 1040, 1042, 1049, 1052, 1053, 1054, 1100, 1154, 1165, 1172, 1185, 1207, 1210, 1217, 1219, 1233, 1234, 1236, 1302, 1326, 1353, 1354, 1356, 1357, 1379, 1380, 1383, 1393, 1394, 1395, 1398, 1404, 1406, 1407, 1408, 1409, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426], "compressor": [10, 690, 786], "do": [10, 55, 75, 88, 92, 93, 94, 96, 99, 101, 102, 106, 107, 109, 111, 115, 133, 165, 184, 199, 202, 204, 230, 231, 238, 243, 277, 278, 280, 362, 380, 410, 411, 412, 418, 419, 458, 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1426], "add_edges_from": [10, 15, 16, 36, 41, 70, 82, 89, 115, 132, 151, 158, 165, 199, 204, 207, 236, 248, 287, 327, 376, 422, 423, 425, 426, 460, 469, 501, 511, 512, 572, 574, 588, 688, 690, 705, 706, 707, 709, 730, 742, 743, 796, 853, 857, 862, 887, 891, 892, 898, 902, 907, 925, 927, 928, 934, 938, 943, 956, 962, 963, 968, 972, 973, 979, 983, 988, 999, 1002, 1003, 1007, 1009, 1010, 1037, 1039, 1040, 1070, 1085, 1094, 1135, 1154, 1217, 1287, 1291, 1326, 1404, 1407, 1426], "base_opt": [10, 15], "dict": [10, 15, 19, 25, 39, 54, 57, 58, 67, 70, 88, 101, 102, 107, 109, 144, 145, 148, 157, 159, 160, 165, 168, 169, 176, 179, 184, 189, 190, 195, 197, 200, 202, 204, 207, 220, 237, 239, 240, 252, 290, 309, 310, 329, 334, 336, 353, 408, 411, 412, 416, 422, 427, 470, 473, 481, 482, 496, 502, 512, 545, 561, 563, 565, 566, 575, 577, 578, 579, 580, 588, 614, 628, 631, 636, 637, 638, 640, 642, 644, 645, 646, 647, 648, 649, 662, 666, 669, 687, 688, 691, 705, 706, 707, 713, 715, 749, 750, 760, 796, 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690, 886, 924, 967, 1006, 1059, 1154, 1271, 1406, 1407, 1426], "nonexp_graph": 10, "compression_nod": 10, "summar": [10, 15, 100, 101, 690, 691, 758, 791, 1325, 1328, 1413], "dedensifi": [10, 758], "threshold": [10, 57, 83, 112, 220, 229, 231, 380, 381, 690, 692, 695, 696, 758, 786, 1117, 1193, 1194, 1196, 1197, 1198, 1325, 1398, 1406, 1407, 1408, 1412, 1414], "copi": [10, 16, 38, 44, 93, 95, 106, 167, 196, 199, 202, 203, 204, 205, 284, 285, 286, 287, 288, 341, 388, 390, 392, 406, 433, 434, 435, 436, 437, 453, 460, 469, 521, 584, 585, 587, 596, 599, 602, 603, 605, 606, 607, 610, 611, 613, 614, 633, 636, 690, 864, 885, 887, 890, 891, 909, 925, 926, 927, 945, 963, 966, 968, 971, 972, 990, 1003, 1007, 1008, 1009, 1035, 1038, 1057, 1061, 1063, 1066, 1082, 1083, 1122, 1183, 1189, 1217, 1223, 1227, 1251, 1270, 1294, 1295, 1296, 1403, 1404, 1406, 1407, 1408, 1409, 1412, 1413, 1422, 1425], "nonexp_node_color": 10, "nonexp_node_s": 10, "yellow": [10, 15, 598, 760, 1426], "nonexp_po": 10, "75": [10, 34, 239, 260, 299, 314, 355, 356, 386, 682, 1169, 1170, 1171, 1173, 1404, 1408, 1426], "c_node": [10, 690], "spot": 10, "247": [10, 17], "plot_dedensif": [10, 17], "153": [11, 455], "curiou": 11, "let": [11, 55, 58, 93, 97, 101, 103, 217, 257, 280, 282, 299, 300, 313, 322, 371, 372, 383, 586, 619, 762, 1219, 1278, 1279, 1326, 1425], "defin": [11, 24, 52, 58, 69, 97, 112, 127, 213, 222, 223, 239, 240, 260, 261, 262, 263, 285, 289, 311, 316, 329, 334, 335, 345, 346, 347, 356, 385, 386, 390, 424, 425, 426, 429, 432, 433, 434, 435, 436, 437, 449, 464, 465, 466, 469, 494, 495, 498, 499, 500, 502, 503, 506, 507, 509, 510, 519, 567, 569, 570, 571, 573, 574, 575, 577, 586, 614, 615, 619, 621, 625, 652, 671, 673, 674, 676, 684, 685, 686, 687, 688, 689, 728, 730, 738, 751, 752, 753, 762, 791, 796, 1037, 1038, 1039, 1040, 1045, 1047, 1071, 1081, 1098, 1147, 1154, 1170, 1172, 1195, 1197, 1280, 1286, 1287, 1288, 1296, 1320, 1321, 1326, 1344, 1353, 1354, 1359, 1363, 1379, 1395, 1402, 1407, 1408, 1412, 1426], "an": [11, 15, 24, 25, 31, 34, 38, 41, 44, 46, 49, 52, 54, 55, 58, 63, 66, 67, 71, 75, 76, 77, 88, 91, 92, 93, 94, 95, 96, 99, 100, 101, 102, 103, 104, 107, 110, 112, 114, 115, 116, 120, 121, 127, 128, 132, 141, 151, 152, 157, 158, 160, 165, 166, 167, 168, 170, 175, 179, 180, 181, 184, 188, 189, 191, 192, 193, 194, 195, 198, 199, 201, 204, 206, 207, 208, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 226, 227, 229, 230, 231, 232, 235, 238, 239, 240, 243, 249, 250, 251, 255, 256, 264, 266, 267, 269, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 291, 292, 293, 294, 295, 297, 298, 299, 301, 302, 306, 307, 308, 309, 311, 312, 315, 316, 318, 319, 320, 322, 324, 325, 326, 329, 330, 332, 341, 342, 343, 345, 346, 347, 348, 349, 350, 351, 353, 357, 362, 363, 364, 365, 366, 370, 373, 374, 375, 377, 378, 379, 380, 381, 383, 384, 385, 387, 388, 389, 390, 392, 394, 395, 400, 402, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 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748, 752, 760, 761, 762, 767, 775, 782, 791, 796, 801, 806, 810, 814, 818, 822, 827, 832, 837, 842, 847, 849, 850, 851, 853, 854, 856, 857, 859, 862, 863, 864, 865, 866, 869, 871, 872, 873, 877, 878, 880, 881, 882, 883, 884, 886, 887, 889, 891, 892, 894, 895, 896, 898, 899, 901, 902, 904, 907, 908, 909, 910, 911, 914, 915, 916, 920, 921, 922, 923, 924, 925, 927, 928, 930, 931, 932, 934, 935, 937, 938, 940, 943, 944, 945, 946, 947, 948, 950, 952, 953, 954, 955, 959, 960, 961, 962, 963, 964, 965, 967, 968, 970, 972, 973, 975, 976, 977, 979, 980, 982, 983, 985, 988, 989, 990, 991, 992, 993, 995, 996, 997, 998, 1002, 1003, 1004, 1005, 1006, 1007, 1009, 1010, 1012, 1013, 1018, 1020, 1036, 1037, 1038, 1039, 1040, 1042, 1043, 1045, 1046, 1049, 1050, 1051, 1061, 1062, 1066, 1068, 1074, 1075, 1081, 1082, 1084, 1085, 1086, 1087, 1088, 1090, 1094, 1098, 1099, 1100, 1101, 1102, 1103, 1105, 1115, 1117, 1122, 1133, 1135, 1137, 1143, 1144, 1146, 1149, 1150, 1151, 1152, 1154, 1155, 1157, 1159, 1160, 1163, 1166, 1167, 1175, 1177, 1178, 1179, 1181, 1182, 1185, 1186, 1187, 1188, 1192, 1193, 1194, 1195, 1196, 1197, 1198, 1199, 1202, 1205, 1206, 1207, 1208, 1209, 1210, 1211, 1212, 1213, 1216, 1217, 1218, 1222, 1224, 1225, 1227, 1228, 1229, 1230, 1232, 1234, 1235, 1236, 1239, 1242, 1244, 1250, 1259, 1262, 1263, 1267, 1269, 1270, 1271, 1272, 1273, 1275, 1276, 1277, 1278, 1279, 1281, 1282, 1287, 1288, 1291, 1294, 1295, 1296, 1300, 1302, 1303, 1319, 1320, 1321, 1323, 1324, 1326, 1328, 1329, 1331, 1333, 1334, 1336, 1341, 1344, 1352, 1362, 1363, 1365, 1371, 1377, 1378, 1379, 1380, 1381, 1383, 1387, 1393, 1394, 1395, 1397, 1398, 1399, 1402, 1404, 1405, 1406, 1407, 1408, 1409, 1410, 1412, 1413, 1414, 1416, 1417, 1424, 1425, 1426], "process": [11, 13, 52, 76, 92, 93, 94, 96, 97, 98, 102, 104, 180, 222, 226, 232, 274, 331, 338, 373, 383, 405, 406, 440, 455, 464, 465, 466, 592, 624, 691, 760, 786, 871, 914, 952, 995, 1045, 1100, 1175, 1177, 1180, 1216, 1219, 1222, 1225, 1245, 1280, 1290, 1295, 1296, 1299, 1301, 1383, 1395, 1407, 1408, 1412, 1413, 1414, 1419, 1426], "follow": [11, 25, 44, 49, 52, 53, 65, 67, 83, 86, 91, 92, 93, 94, 95, 97, 99, 100, 101, 102, 103, 108, 110, 111, 128, 132, 151, 161, 171, 183, 207, 213, 227, 229, 230, 231, 243, 280, 305, 338, 343, 351, 362, 373, 378, 380, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 440, 452, 453, 465, 466, 496, 502, 503, 504, 505, 506, 507, 508, 588, 598, 599, 602, 615, 636, 679, 748, 750, 760, 762, 791, 853, 867, 892, 898, 912, 928, 934, 948, 973, 979, 993, 1010, 1102, 1103, 1105, 1144, 1165, 1175, 1179, 1185, 1188, 1200, 1201, 1209, 1219, 1225, 1233, 1234, 1241, 1251, 1260, 1274, 1275, 1276, 1277, 1281, 1296, 1315, 1323, 1326, 1328, 1329, 1388, 1393, 1395, 1399, 1404, 1406, 1407, 1409, 1411, 1412, 1413, 1425, 1426], "given": [11, 38, 44, 62, 64, 67, 91, 99, 101, 103, 112, 116, 141, 142, 144, 152, 158, 193, 197, 208, 211, 212, 227, 229, 235, 236, 248, 249, 260, 264, 266, 269, 271, 273, 274, 276, 279, 281, 283, 284, 285, 286, 287, 288, 320, 329, 331, 338, 344, 351, 353, 357, 362, 363, 364, 365, 373, 378, 380, 381, 385, 439, 454, 455, 460, 462, 470, 477, 478, 480, 497, 511, 512, 513, 559, 560, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 576, 578, 579, 580, 588, 589, 590, 614, 615, 616, 622, 623, 659, 660, 661, 662, 676, 677, 678, 679, 681, 683, 684, 686, 690, 691, 693, 697, 698, 699, 700, 702, 703, 704, 706, 707, 708, 709, 728, 729, 730, 731, 732, 739, 748, 753, 761, 782, 786, 854, 857, 882, 899, 902, 921, 935, 938, 963, 980, 983, 1003, 1046, 1085, 1086, 1094, 1101, 1102, 1135, 1144, 1151, 1162, 1175, 1176, 1177, 1178, 1179, 1180, 1181, 1189, 1199, 1200, 1201, 1206, 1207, 1208, 1209, 1210, 1221, 1222, 1240, 1269, 1273, 1274, 1276, 1295, 1300, 1302, 1315, 1323, 1353, 1354, 1379, 1380, 1394, 1395, 1406], "digit": [11, 70, 99], "base": [11, 15, 38, 43, 55, 58, 69, 93, 94, 100, 101, 102, 103, 107, 128, 132, 199, 203, 205, 212, 216, 220, 229, 296, 297, 301, 302, 303, 308, 309, 310, 311, 312, 322, 323, 324, 325, 329, 330, 337, 343, 346, 347, 362, 371, 373, 374, 380, 381, 382, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 423, 425, 426, 427, 428, 430, 431, 449, 464, 466, 494, 498, 499, 500, 509, 510, 545, 555, 564, 566, 569, 574, 581, 614, 616, 660, 667, 680, 688, 691, 704, 706, 707, 708, 710, 711, 712, 713, 714, 715, 717, 732, 738, 758, 761, 762, 786, 791, 796, 887, 925, 934, 935, 968, 979, 980, 1007, 1036, 1037, 1038, 1041, 1043, 1082, 1088, 1182, 1229, 1235, 1253, 1267, 1296, 1320, 1321, 1323, 1326, 1383, 1387, 1392, 1395, 1402, 1403, 1404, 1406, 1407, 1408, 1409, 1411, 1412, 1421, 1425], "obtain": [11, 91, 165, 207, 282, 345, 346, 347, 380, 383, 387, 388, 389, 390, 394, 465, 511, 606, 618, 619, 656, 722, 742, 743, 760, 796, 862, 892, 907, 928, 943, 973, 988, 1010, 1037, 1039, 1040, 1164, 1253, 1272, 1278, 1279, 1323, 1326, 1356, 1357, 1402, 1426], "seri": [11, 444, 616, 680, 1215, 1286], "finit": [11, 462, 494, 495, 498, 499, 502, 503, 506, 507, 509, 510, 514, 518, 1177, 1179, 1192, 1222], "end": [11, 25, 36, 52, 95, 101, 106, 153, 154, 206, 215, 227, 267, 268, 300, 332, 333, 342, 371, 372, 427, 614, 618, 619, 626, 627, 631, 632, 634, 635, 636, 639, 640, 650, 651, 652, 653, 654, 655, 660, 664, 667, 677, 678, 680, 734, 736, 1038, 1061, 1066, 1075, 1080, 1082, 1084, 1117, 1133, 1135, 1152, 1165, 1206, 1229, 1326, 1333, 1334, 1337, 1338, 1339, 1340, 1342, 1344, 1350, 1353, 1357, 1358, 1368, 1371, 1372, 1375, 1376, 1379, 1404, 1413], "In": [11, 16, 27, 43, 54, 57, 58, 88, 92, 93, 94, 95, 97, 99, 100, 101, 103, 110, 115, 127, 132, 133, 175, 184, 199, 217, 229, 230, 231, 235, 240, 257, 258, 259, 278, 283, 286, 288, 289, 299, 311, 312, 324, 325, 329, 350, 357, 378, 379, 380, 410, 413, 414, 415, 422, 429, 443, 447, 450, 458, 460, 494, 498, 499, 501, 510, 565, 568, 572, 574, 590, 591, 615, 619, 621, 652, 653, 654, 657, 658, 663, 670, 675, 676, 690, 691, 701, 703, 717, 718, 719, 730, 732, 740, 741, 742, 743, 761, 762, 767, 770, 789, 791, 796, 869, 873, 887, 916, 925, 954, 955, 968, 997, 998, 1007, 1037, 1038, 1039, 1040, 1042, 1043, 1066, 1100, 1101, 1117, 1154, 1168, 1199, 1203, 1206, 1207, 1208, 1210, 1216, 1217, 1222, 1226, 1231, 1233, 1241, 1295, 1296, 1300, 1320, 1321, 1326, 1328, 1350, 1394, 1398, 1399, 1404, 1405, 1406, 1407, 1408, 1409, 1413, 1414, 1426], "languag": [11, 92, 99, 110, 1042, 1324, 1341, 1342, 1344, 1381, 1382, 1383, 1411], "discret": [11, 104, 235, 249, 362, 409, 513, 517, 518, 618, 1164, 1165, 1178, 1180, 1186, 1190, 1204, 1278, 1279, 1282, 1314, 1315, 1323, 1406], "global": [11, 103, 314, 341, 410, 477, 486, 487, 509, 592, 1045, 1269, 1296, 1301, 1304, 1305, 1328, 1407, 1409, 1411], "attractor": [11, 388], "map": [11, 34, 38, 52, 67, 101, 102, 103, 115, 125, 144, 145, 148, 166, 169, 197, 238, 243, 264, 350, 369, 391, 412, 416, 417, 418, 419, 423, 424, 425, 426, 431, 440, 460, 530, 531, 534, 540, 541, 544, 545, 559, 560, 561, 563, 588, 614, 670, 676, 678, 751, 752, 760, 762, 863, 908, 944, 947, 989, 992, 1012, 1013, 1018, 1019, 1038, 1039, 1040, 1045, 1133, 1135, 1137, 1217, 1269, 1295, 1296, 1306, 1310, 1317, 1318, 1324, 1325, 1361, 1362, 1393, 1402, 1406, 1408, 1412, 1413, 1425, 1426], "restrict": [11, 102, 128, 353, 791, 1038, 1082, 1404], "For": [11, 54, 67, 88, 92, 93, 95, 97, 99, 101, 102, 103, 105, 107, 110, 115, 125, 128, 132, 143, 151, 158, 159, 160, 165, 168, 185, 189, 199, 200, 204, 226, 230, 231, 235, 238, 239, 240, 246, 247, 255, 259, 282, 297, 298, 299, 301, 302, 304, 306, 307, 308, 309, 311, 312, 314, 315, 316, 321, 322, 324, 325, 326, 328, 329, 330, 338, 346, 347, 356, 357, 358, 380, 385, 392, 395, 397, 398, 400, 402, 403, 404, 407, 410, 411, 412, 413, 414, 416, 417, 418, 419, 422, 429, 431, 432, 433, 434, 435, 436, 450, 453, 460, 479, 480, 488, 494, 495, 496, 498, 499, 502, 503, 506, 507, 509, 510, 522, 523, 524, 555, 565, 568, 572, 574, 585, 587, 598, 614, 615, 618, 619, 625, 633, 636, 641, 643, 659, 677, 678, 686, 687, 688, 691, 717, 718, 719, 733, 734, 735, 736, 737, 742, 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383, 387, 389, 390, 394, 410, 411, 412, 418, 419, 494, 498, 499, 510, 564, 566, 626, 627, 632, 640, 643, 652, 693, 711, 758, 1188, 1207, 1210, 1407], "orbit": 11, "up": [11, 70, 80, 93, 94, 97, 99, 100, 101, 104, 107, 132, 133, 346, 347, 377, 423, 427, 509, 530, 540, 577, 619, 652, 653, 657, 748, 1036, 1038, 1061, 1066, 1082, 1088, 1102, 1144, 1148, 1173, 1213, 1215, 1272, 1326, 1328, 1355, 1358, 1395, 1396, 1402, 1404, 1406, 1410, 1411, 1413, 1414, 1416, 1417, 1420, 1426], "reveal": [11, 711, 786], "maximum": [11, 112, 115, 209, 210, 211, 212, 214, 215, 217, 222, 224, 227, 257, 259, 264, 277, 278, 279, 281, 288, 296, 304, 311, 312, 315, 316, 317, 318, 319, 321, 324, 328, 330, 339, 341, 342, 343, 346, 347, 352, 356, 361, 373, 377, 380, 382, 383, 385, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 428, 440, 472, 473, 494, 498, 499, 500, 501, 502, 503, 506, 507, 509, 510, 520, 521, 564, 566, 581, 583, 589, 591, 592, 670, 671, 672, 673, 674, 676, 691, 693, 694, 704, 706, 707, 708, 710, 711, 712, 713, 714, 715, 716, 720, 723, 724, 732, 734, 735, 736, 737, 740, 741, 749, 758, 768, 791, 1117, 1133, 1135, 1137, 1165, 1181, 1198, 1199, 1200, 1201, 1208, 1225, 1237, 1238, 1302, 1323, 1395, 1402, 1406, 1407, 1412, 1413], "cycl": [11, 38, 44, 95, 120, 214, 227, 228, 229, 230, 231, 232, 263, 293, 294, 295, 338, 341, 343, 358, 449, 450, 451, 452, 453, 457, 462, 463, 464, 466, 467, 468, 480, 496, 501, 504, 505, 508, 519, 584, 585, 587, 608, 628, 629, 630, 632, 652, 657, 658, 663, 697, 727, 742, 743, 758, 791, 1043, 1052, 1135, 1137, 1148, 1149, 1152, 1163, 1186, 1190, 1242, 1244, 1260, 1264, 1325, 1395, 1397, 1398, 1401, 1403, 1404, 1406, 1407, 1408, 1411, 1412, 1414, 1424, 1425], "requir": [11, 38, 65, 93, 94, 95, 99, 100, 101, 102, 104, 106, 107, 109, 111, 115, 165, 207, 291, 292, 293, 296, 301, 302, 308, 309, 316, 437, 476, 500, 520, 521, 615, 680, 698, 699, 700, 720, 729, 731, 786, 791, 796, 862, 892, 907, 928, 943, 973, 988, 1010, 1037, 1039, 1040, 1046, 1111, 1143, 1192, 1193, 1199, 1215, 1217, 1235, 1296, 1326, 1345, 1348, 1349, 1350, 1382, 1393, 1394, 1396, 1402, 1405, 1406, 1407, 1408, 1411, 1412, 1413, 1414, 1419, 1420, 1425, 1426], "less": [11, 34, 43, 99, 101, 128, 142, 144, 227, 289, 324, 325, 380, 381, 383, 384, 385, 422, 425, 426, 429, 464, 520, 521, 636, 673, 686, 731, 786, 1135, 1162, 1174, 1185, 1187, 1194, 1275, 1276, 1353, 1354, 1379, 1404, 1405, 1408, 1411, 1413, 1414], "smallest": [11, 31, 211, 221, 264, 362, 370, 376, 381, 440, 483, 490, 679, 729, 731, 1048, 1200, 1249, 1259, 1275, 1276, 1302, 1320, 1321, 1407], "177": [11, 297, 298, 306, 307, 329], "e": [11, 15, 16, 31, 34, 38, 46, 52, 61, 65, 67, 69, 71, 76, 82, 89, 91, 92, 93, 94, 95, 97, 99, 101, 102, 103, 104, 107, 110, 111, 112, 115, 127, 141, 144, 151, 152, 157, 158, 168, 170, 171, 177, 189, 192, 195, 207, 211, 217, 218, 221, 226, 233, 236, 241, 244, 248, 249, 267, 275, 278, 280, 282, 284, 288, 289, 290, 293, 295, 300, 301, 302, 305, 306, 307, 308, 309, 311, 312, 313, 322, 324, 325, 326, 331, 332, 333, 339, 340, 341, 343, 345, 355, 356, 358, 361, 371, 372, 374, 378, 383, 385, 398, 405, 406, 429, 434, 449, 452, 453, 455, 467, 468, 469, 471, 472, 474, 475, 476, 479, 488, 490, 491, 492, 494, 496, 498, 499, 502, 503, 504, 505, 506, 507, 508, 509, 510, 517, 518, 565, 566, 575, 577, 582, 586, 588, 590, 593, 598, 602, 615, 616, 618, 619, 625, 626, 675, 677, 678, 686, 688, 691, 692, 693, 732, 734, 736, 762, 796, 850, 853, 854, 856, 857, 865, 866, 867, 878, 881, 884, 892, 895, 898, 899, 901, 902, 910, 911, 912, 920, 923, 928, 931, 934, 935, 937, 938, 946, 947, 948, 960, 962, 965, 973, 976, 979, 980, 982, 983, 984, 991, 992, 993, 1002, 1005, 1010, 1037, 1038, 1039, 1040, 1047, 1097, 1100, 1104, 1133, 1134, 1135, 1136, 1146, 1154, 1165, 1175, 1177, 1179, 1180, 1182, 1183, 1184, 1187, 1192, 1193, 1194, 1203, 1204, 1205, 1207, 1210, 1219, 1222, 1226, 1230, 1233, 1234, 1260, 1266, 1268, 1278, 1279, 1280, 1287, 1288, 1292, 1295, 1302, 1303, 1310, 1320, 1321, 1323, 1326, 1329, 1333, 1337, 1338, 1341, 1344, 1356, 1388, 1393, 1396, 1402, 1403, 1405, 1406, 1407, 1409, 1411, 1413, 1414, 1417], "687": 11, "1071": 11, "345": 11, "216": [11, 1193], "225": [11, 89, 207, 278, 892, 928, 973, 1010, 1155], "141": [11, 226], "66": [11, 34, 58, 64, 566], "432": 11, "99": [11, 65, 592, 1201, 1233, 1323, 1403], "1458": 11, "702": 11, "351": 11, "test": [11, 52, 88, 94, 95, 96, 97, 99, 103, 106, 109, 132, 180, 267, 268, 310, 338, 343, 397, 398, 420, 421, 454, 520, 525, 535, 555, 616, 671, 740, 741, 742, 743, 755, 757, 760, 762, 871, 914, 952, 995, 1042, 1070, 1072, 1165, 1326, 1333, 1334, 1337, 1339, 1340, 1344, 1349, 1350, 1371, 1372, 1375, 1376, 1393, 1395, 1396, 1398, 1401, 1405, 1406, 1407, 1408, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1423, 1424, 1425, 1426], "softwar": [11, 91, 107, 111, 481, 482, 729, 731, 1426], "power": [11, 45, 94, 110, 207, 311, 312, 324, 371, 372, 520, 521, 564, 566, 692, 758, 892, 928, 973, 1010, 1043, 1165, 1175, 1237, 1238, 1255, 1316, 1319, 1395, 1406, 1407, 1426], "abov": [11, 92, 93, 100, 101, 102, 103, 110, 291, 292, 315, 316, 325, 330, 380, 383, 386, 453, 460, 491, 494, 498, 499, 502, 503, 509, 510, 521, 686, 692, 730, 762, 1038, 1102, 1148, 1165, 1185, 1219, 1234, 1274, 1278, 1279, 1300, 1399, 1404, 1407, 1417], "correspond": [11, 67, 101, 103, 144, 161, 167, 222, 223, 227, 228, 229, 230, 231, 232, 233, 234, 265, 266, 281, 311, 312, 324, 325, 331, 332, 350, 361, 362, 380, 391, 415, 417, 418, 419, 422, 460, 476, 482, 511, 514, 581, 583, 588, 609, 615, 616, 624, 628, 629, 630, 677, 678, 679, 728, 729, 731, 732, 742, 743, 748, 791, 850, 864, 895, 909, 931, 945, 976, 990, 1098, 1099, 1101, 1102, 1103, 1105, 1109, 1115, 1135, 1143, 1144, 1175, 1177, 1178, 1179, 1180, 1181, 1193, 1194, 1212, 1222, 1271, 1272, 1274, 1276, 1277, 1278, 1279, 1281, 1323, 1332, 1333, 1335, 1336, 1355, 1358, 1359, 1360, 1363, 1364, 1370, 1394, 1405, 1406], "below": [11, 13, 25, 92, 94, 99, 100, 111, 151, 206, 330, 383, 408, 410, 411, 412, 413, 414, 415, 417, 419, 429, 464, 491, 492, 494, 496, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 565, 615, 692, 796, 853, 898, 934, 979, 1037, 1039, 1040, 1117, 1144, 1175, 1177, 1217, 1222, 1242, 1275, 1276, 1277, 1296, 1349, 1393, 1402, 1404, 1417, 1426], "powersum": 11, "over": [11, 34, 38, 49, 71, 88, 94, 95, 99, 101, 102, 103, 109, 152, 157, 158, 159, 160, 168, 175, 176, 180, 181, 184, 188, 189, 190, 191, 195, 200, 201, 213, 214, 220, 230, 235, 291, 295, 299, 314, 315, 316, 320, 329, 330, 345, 346, 347, 362, 363, 364, 365, 369, 373, 374, 381, 385, 408, 409, 429, 477, 488, 489, 496, 497, 523, 526, 529, 533, 536, 539, 543, 598, 636, 678, 690, 703, 704, 705, 706, 707, 708, 710, 711, 719, 733, 734, 736, 738, 762, 849, 851, 854, 856, 857, 858, 859, 865, 869, 870, 871, 872, 873, 877, 878, 879, 880, 884, 888, 889, 894, 896, 899, 901, 902, 903, 904, 910, 914, 915, 916, 923, 930, 932, 935, 937, 938, 939, 940, 946, 951, 952, 953, 955, 960, 961, 965, 969, 970, 975, 977, 980, 982, 983, 984, 985, 991, 995, 996, 998, 1005, 1074, 1075, 1084, 1100, 1192, 1217, 1225, 1233, 1241, 1278, 1279, 1288, 1326, 1328, 1393, 1402, 1404, 1405, 1407, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1425, 1426], "converg": [11, 311, 324, 373, 564, 565, 566, 676, 1043, 1407, 1408], "singl": [11, 13, 58, 80, 93, 94, 99, 101, 102, 104, 107, 143, 151, 152, 156, 158, 166, 168, 175, 176, 180, 188, 189, 193, 220, 265, 274, 290, 293, 294, 299, 315, 322, 327, 331, 344, 353, 354, 391, 393, 424, 427, 443, 462, 464, 491, 494, 498, 499, 502, 503, 509, 510, 577, 584, 585, 587, 598, 621, 635, 660, 661, 662, 677, 678, 690, 705, 742, 743, 786, 791, 796, 853, 854, 855, 857, 863, 865, 869, 870, 871, 877, 878, 882, 898, 899, 900, 902, 908, 910, 914, 921, 934, 935, 936, 938, 944, 946, 950, 951, 952, 959, 960, 962, 963, 979, 980, 981, 983, 989, 991, 995, 1002, 1003, 1037, 1039, 1040, 1041, 1045, 1046, 1058, 1085, 1086, 1091, 1092, 1093, 1097, 1098, 1099, 1101, 1102, 1104, 1120, 1126, 1133, 1135, 1137, 1140, 1147, 1151, 1156, 1164, 1167, 1172, 1189, 1197, 1272, 1274, 1295, 1296, 1318, 1320, 1321, 1323, 1324, 1328, 1331, 1334, 1335, 1345, 1363, 1364, 1369, 1401, 1404, 1406, 1407, 1409, 1412, 1413], "fix": [11, 91, 93, 94, 95, 100, 106, 512, 693, 694, 709, 1117, 1269, 1394, 1396, 1400, 1402, 1403, 1407, 1408, 1410, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425], "appli": [11, 35, 52, 88, 92, 99, 220, 230, 231, 299, 322, 339, 340, 342, 358, 462, 509, 586, 588, 618, 625, 647, 786, 791, 1036, 1085, 1086, 1088, 1094, 1135, 1137, 1164, 1188, 1197, 1242, 1269, 1282, 1296, 1323, 1356, 1357, 1394, 1404, 1407, 1425], "lead": [11, 99, 101, 230, 231, 383, 471, 472, 473, 474, 475, 567, 1175, 1177, 1222, 1326, 1405, 1426], "370": [11, 1244], "371": [11, 274], "407": [11, 346, 347], "modulo": [11, 586, 1190], "ad": [11, 16, 27, 41, 71, 88, 94, 95, 96, 97, 99, 100, 101, 102, 103, 127, 141, 151, 152, 153, 154, 155, 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1358, 1412], "differ": [15, 25, 27, 28, 33, 41, 53, 54, 57, 63, 71, 86, 92, 93, 94, 95, 99, 103, 112, 161, 164, 165, 204, 207, 215, 216, 223, 280, 282, 297, 298, 314, 315, 326, 330, 334, 335, 337, 341, 358, 361, 371, 372, 373, 374, 378, 410, 413, 414, 415, 435, 437, 509, 511, 512, 593, 602, 615, 704, 717, 718, 738, 750, 758, 772, 786, 862, 891, 892, 907, 928, 943, 972, 973, 988, 1010, 1102, 1105, 1133, 1165, 1169, 1170, 1171, 1193, 1198, 1207, 1255, 1269, 1287, 1296, 1326, 1365, 1366, 1382, 1394, 1404, 1405, 1406, 1413, 1414, 1425, 1426], "relat": [15, 34, 67, 92, 93, 95, 99, 100, 115, 129, 132, 220, 230, 297, 366, 370, 586, 588, 619, 688, 762, 767, 795, 1202, 1205, 1269, 1323, 1395, 1402, 1406, 1413, 1416, 1425], "strong": [15, 397, 511, 512, 517, 610, 619, 691, 699, 758, 1408], "weak": [15, 398, 691, 758, 1425], "number_of_nod": [15, 25, 80, 156, 187, 311, 324, 337, 383, 564, 581, 852, 855, 876, 897, 900, 919, 933, 936, 958, 978, 981, 1001, 1154, 1271, 1426], "7482934": 15, "_": [15, 16, 26, 38, 93, 105, 300, 333, 356, 372, 405, 406, 425, 426, 502, 503, 506, 507, 569, 588, 630, 1352, 1354, 1378, 1380, 1411], "edge_type_visual_weight_lookup": 15, "edge_weight": [15, 382, 583], "node_attribut": [15, 691], "edge_attribut": [15, 283, 691, 1101], "summary_graph": [15, 691], "snap_aggreg": [15, 758, 1413], "prefix": [15, 67, 512, 690, 691, 1272, 1326, 1347, 1413, 1421], "aggreg": [15, 511, 512, 691, 786], "summary_po": 15, "8375428": 15, "edge_typ": 15, "get_edge_data": [15, 25, 1411], "178": [15, 17], "plot_snap": [15, 17], "support": [16, 52, 77, 92, 93, 96, 100, 101, 102, 103, 226, 308, 322, 339, 340, 342, 343, 356, 373, 410, 411, 412, 418, 419, 464, 494, 496, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 597, 626, 627, 632, 633, 635, 636, 690, 738, 762, 775, 786, 796, 1037, 1038, 1039, 1040, 1114, 1116, 1302, 1326, 1341, 1342, 1344, 1353, 1354, 1355, 1356, 1357, 1358, 1379, 1380, 1381, 1383, 1387, 1394, 1395, 1396, 1398, 1402, 1404, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426], "unsupport": 16, "contain": [16, 25, 34, 45, 65, 69, 71, 88, 99, 102, 104, 114, 115, 151, 152, 157, 158, 165, 166, 167, 168, 172, 175, 176, 177, 180, 188, 189, 193, 195, 199, 207, 212, 214, 220, 226, 236, 237, 238, 240, 241, 243, 245, 248, 249, 252, 253, 255, 256, 257, 258, 259, 260, 264, 266, 267, 270, 277, 278, 280, 281, 290, 293, 294, 299, 315, 320, 322, 338, 344, 346, 347, 350, 352, 353, 355, 356, 357, 358, 360, 373, 377, 379, 380, 381, 388, 400, 408, 414, 415, 427, 432, 433, 437, 440, 457, 481, 482, 494, 495, 498, 499, 500, 502, 503, 506, 507, 509, 510, 512, 513, 514, 516, 564, 568, 572, 574, 589, 593, 596, 599, 602, 621, 624, 631, 632, 652, 656, 658, 660, 661, 662, 687, 688, 689, 695, 723, 724, 725, 726, 749, 786, 796, 853, 854, 856, 857, 862, 863, 864, 865, 868, 869, 870, 871, 877, 878, 882, 884, 887, 892, 898, 899, 901, 902, 907, 908, 909, 910, 913, 914, 921, 923, 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192, 453, 602, 881, 920, 962, 1002, 1175, 1177, 1222, 1393, 1394, 1412, 1420, 1426], "nbr": [16, 88, 159, 190, 199, 200, 207, 229, 230, 231, 285, 500, 506, 796, 858, 879, 887, 888, 892, 903, 925, 928, 939, 968, 969, 973, 984, 1007, 1010, 1037, 1039, 1040, 1094, 1326, 1404, 1426], "adj": [16, 88, 199, 200, 207, 324, 325, 796, 849, 887, 888, 892, 894, 915, 925, 928, 930, 968, 969, 973, 975, 996, 1007, 1010, 1037, 1039, 1040, 1094, 1326, 1404, 1411, 1417, 1425, 1426], "g_minus_h": 16, "strip": [16, 25, 69, 1215], "_node_color": 16, "_po": 16, "draw_networkx_edg": [16, 25, 26, 27, 28, 33, 35, 38, 39, 40, 41, 44, 46, 68, 83, 1130, 1133, 1134, 1136, 1137, 1411, 1413], "draw_networkx_label": [16, 25, 35, 38, 46, 71, 1130, 1133, 1134, 1135, 1137], "ncl": 16, "undirect": [16, 25, 34, 71, 93, 112, 177, 185, 204, 205, 209, 211, 212, 214, 215, 216, 217, 218, 219, 220, 221, 224, 227, 228, 229, 230, 231, 232, 237, 239, 240, 246, 247, 264, 267, 275, 277, 278, 280, 281, 293, 294, 295, 297, 298, 300, 313, 315, 318, 319, 321, 322, 328, 330, 331, 332, 333, 337, 338, 341, 345, 346, 347, 348, 349, 350, 352, 353, 371, 372, 379, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 428, 430, 431, 437, 439, 440, 450, 463, 464, 465, 466, 467, 478, 479, 480, 481, 482, 485, 486, 487, 488, 490, 491, 492, 500, 559, 560, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 581, 582, 583, 590, 594, 595, 598, 600, 601, 605, 606, 607, 610, 611, 613, 615, 618, 619, 624, 625, 652, 658, 681, 682, 683, 684, 686, 687, 688, 689, 692, 694, 717, 718, 727, 730, 731, 732, 734, 735, 736, 737, 738, 742, 743, 753, 760, 761, 762, 767, 779, 791, 874, 891, 917, 927, 956, 972, 999, 1009, 1036, 1038, 1056, 1060, 1088, 1090, 1098, 1101, 1115, 1133, 1135, 1166, 1167, 1173, 1175, 1182, 1184, 1187, 1189, 1190, 1191, 1193, 1196, 1197, 1198, 1199, 1202, 1206, 1207, 1217, 1219, 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609, 635, 636, 660, 671, 672, 673, 674, 676, 686, 691, 692, 704, 705, 706, 707, 708, 710, 711, 712, 713, 714, 715, 716, 721, 722, 751, 760, 853, 854, 856, 857, 864, 873, 874, 882, 892, 898, 899, 901, 902, 909, 916, 917, 921, 928, 934, 935, 937, 938, 945, 947, 948, 955, 956, 962, 963, 973, 979, 980, 982, 983, 990, 992, 993, 998, 999, 1002, 1003, 1010, 1043, 1061, 1070, 1071, 1072, 1081, 1094, 1095, 1096, 1098, 1099, 1104, 1117, 1130, 1133, 1134, 1135, 1136, 1137, 1151, 1154, 1165, 1175, 1177, 1178, 1181, 1182, 1189, 1193, 1196, 1197, 1198, 1199, 1202, 1207, 1210, 1211, 1212, 1219, 1222, 1235, 1242, 1275, 1276, 1277, 1278, 1279, 1294, 1295, 1296, 1297, 1300, 1315, 1323, 1324, 1326, 1328, 1331, 1334, 1336, 1337, 1338, 1339, 1340, 1341, 1344, 1345, 1348, 1349, 1350, 1356, 1357, 1360, 1363, 1364, 1382, 1393, 1397, 1398, 1399, 1402, 1403, 1404, 1406, 1407, 1412, 1416, 1426], "to_undirect": [16, 25, 69, 796, 1037, 1039, 1040, 1182, 1184, 1404, 1413, 1426], "magenta": 16, "six": 16, "classifi": [16, 512, 684, 750], "four": [16, 23, 47, 86, 99, 102, 165, 263, 585, 587, 692, 862, 907, 943, 988, 1039, 1040, 1164, 1193, 1199, 1211, 1323, 1407, 1408, 1414, 1426], "green": [16, 32, 38, 70, 93, 115, 464, 598, 760, 1302, 1330, 1394, 1412, 1426], "goal": [16, 88, 92, 99, 105, 107, 127, 383, 626, 627, 717, 718, 1042], "g_ex": 16, "m": [16, 25, 28, 30, 31, 63, 65, 67, 91, 93, 96, 102, 106, 110, 112, 128, 181, 191, 201, 209, 211, 212, 219, 227, 231, 235, 236, 238, 239, 240, 241, 243, 244, 248, 257, 258, 259, 263, 272, 274, 275, 278, 280, 282, 284, 293, 294, 296, 300, 301, 302, 308, 309, 315, 316, 317, 330, 338, 341, 343, 345, 352, 355, 356, 361, 362, 370, 380, 383, 385, 412, 429, 431, 432, 433, 451, 462, 479, 494, 498, 499, 509, 510, 511, 512, 519, 545, 555, 569, 582, 584, 585, 587, 588, 606, 614, 619, 625, 652, 658, 659, 684, 686, 691, 692, 706, 748, 749, 761, 762, 775, 872, 880, 889, 953, 961, 970, 1060, 1151, 1155, 1157, 1169, 1175, 1177, 1179, 1181, 1199, 1201, 1202, 1203, 1204, 1205, 1207, 1208, 1209, 1210, 1211, 1213, 1215, 1216, 1218, 1219, 1220, 1222, 1223, 1226, 1229, 1230, 1231, 1233, 1234, 1235, 1240, 1256, 1265, 1269, 1271, 1278, 1279, 1280, 1287, 1288, 1292, 1323, 1387, 1406, 1409, 1426], "node_color_list": 16, "nc": [16, 56], "spectral_layout": [16, 43, 1141, 1399, 1406], "subgraphs_of_g_ex": 16, "removed_edg": 16, "node_color_list_c": 16, "One": [16, 52, 55, 101, 102, 103, 115, 545, 559, 560, 679, 684, 761, 1177, 1186, 1272, 1315, 1326, 1404, 1426], "g_ex_r": 16, "compos": [16, 269, 270, 271, 272, 273, 274, 275, 276, 600, 604, 758, 1400, 1406, 1407, 1417, 1423, 1425], "previous": [16, 91, 108, 112, 322, 614, 1182, 1183, 1184, 1395, 1407, 1417], "store": [16, 25, 39, 53, 54, 55, 57, 67, 86, 93, 97, 101, 102, 110, 158, 219, 220, 283, 290, 345, 346, 347, 431, 470, 471, 472, 473, 474, 475, 494, 495, 498, 499, 502, 503, 506, 507, 509, 510, 585, 587, 615, 660, 664, 667, 719, 733, 739, 762, 786, 796, 857, 902, 938, 983, 1037, 1038, 1039, 1040, 1046, 1085, 1086, 1101, 1102, 1104, 1165, 1170, 1193, 1196, 1197, 1198, 1199, 1213, 1215, 1278, 1294, 1296, 1330, 1333, 1334, 1345, 1348, 1349, 1350, 1359, 1360, 1363, 1364, 1365, 1366, 1371, 1382, 1388, 1390, 1394, 1404, 1414], "assert": [16, 67, 88, 102, 1411, 1414, 1424, 1425, 1426], "is_isomorph": [16, 584, 585, 587, 588, 608, 671, 690, 739, 758, 761, 762, 1399, 1406], "673": [16, 17], "plot_subgraph": [16, 17, 1414], "28": [17, 64, 66, 68, 220, 226, 346, 347, 383, 384, 427, 501, 519, 703, 1040, 1109, 1202, 1401, 1403, 1414], "057": 17, "auto_examples_algorithm": 17, "03": [17, 21, 25, 59, 85, 112, 217, 274, 300], "read": [18, 22, 25, 40, 52, 54, 55, 57, 58, 65, 75, 86, 93, 94, 100, 115, 159, 165, 167, 190, 200, 267, 583, 618, 796, 858, 862, 864, 879, 888, 903, 907, 909, 939, 943, 945, 947, 969, 984, 988, 990, 992, 1012, 1013, 1018, 1019, 1020, 1021, 1022, 1035, 1036, 1037, 1038, 1039, 1040, 1042, 1043, 1061, 1066, 1082, 1083, 1088, 1121, 1143, 1144, 1270, 1296, 1325, 1326, 1329, 1330, 1333, 1337, 1338, 1342, 1343, 1345, 1348, 1349, 1350, 1351, 1352, 1354, 1356, 1357, 1367, 1368, 1371, 1375, 1377, 1378, 1380, 1381, 1382, 1383, 1386, 1387, 1388, 1389, 1390, 1394, 1395, 1397, 1398, 1401, 1402, 1404, 1405, 1406, 1407, 1408, 1409, 1410, 1411, 1413, 1414, 1418, 1424, 1425], "write": [18, 22, 49, 52, 75, 76, 77, 86, 89, 93, 99, 105, 110, 115, 267, 268, 470, 1012, 1013, 1018, 1019, 1020, 1021, 1022, 1047, 1123, 1129, 1300, 1325, 1326, 1329, 1330, 1334, 1337, 1339, 1340, 1344, 1345, 1348, 1349, 1350, 1352, 1354, 1357, 1358, 1372, 1375, 1376, 1378, 1380, 1381, 1382, 1383, 1387, 1388, 1390, 1395, 1397, 1398, 1399, 1401, 1402, 1405, 1406, 1411, 1412, 1414, 1425, 1426], "simpl": [18, 22, 23, 32, 47, 86, 93, 94, 97, 100, 103, 109, 110, 132, 184, 220, 229, 230, 231, 249, 287, 293, 300, 304, 313, 321, 328, 332, 333, 338, 343, 371, 372, 373, 380, 381, 423, 425, 438, 452, 453, 468, 479, 481, 482, 490, 496, 500, 504, 505, 508, 514, 517, 518, 594, 608, 624, 632, 677, 678, 679, 680, 686, 693, 758, 775, 780, 796, 873, 916, 955, 998, 1037, 1038, 1039, 1040, 1098, 1099, 1100, 1130, 1133, 1175, 1177, 1180, 1181, 1207, 1208, 1209, 1210, 1217, 1219, 1222, 1252, 1269, 1296, 1323, 1325, 1326, 1328, 1330, 1351, 1352, 1353, 1354, 1382, 1388, 1395, 1401, 1404, 1406, 1407, 1412, 1413, 1421, 1426], "lollipop": [19, 1157, 1426], "vertex": [19, 115, 211, 235, 249, 281, 289, 315, 322, 330, 338, 359, 360, 373, 387, 394, 397, 427, 428, 432, 438, 477, 491, 580, 606, 615, 616, 619, 622, 623, 624, 688, 689, 758, 1164, 1185, 1190, 1206, 1218, 1219, 1222, 1251, 1323, 1326, 1400, 1406, 1407], "length": [19, 39, 52, 67, 102, 120, 151, 232, 288, 295, 297, 298, 299, 306, 307, 310, 314, 315, 316, 320, 322, 326, 327, 329, 330, 332, 333, 341, 343, 345, 346, 347, 371, 372, 383, 384, 451, 459, 462, 467, 469, 470, 473, 513, 515, 516, 517, 520, 521, 591, 592, 627, 628, 629, 630, 632, 633, 636, 637, 638, 640, 641, 642, 643, 644, 646, 647, 648, 649, 651, 652, 653, 654, 655, 656, 658, 660, 661, 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437, 442, 447, 448, 624, 685, 710, 711, 712, 713, 714, 715, 734, 736, 1146, 1157, 1255, 1408, 1409, 1412], "krakauer": 91, "financi": 91, "summer": [91, 105, 1405, 1413, 1414], "foundat": [91, 110, 412, 431, 441, 445, 446, 619, 751], "grant": [91, 100, 105, 1202], "w911nf": 91, "0288": 91, "darpa": 91, "intellig": [91, 132, 494, 574, 590, 732, 762, 1207, 1210], "subcontract": 91, "No": [91, 92, 228, 282, 284, 285, 286, 287, 288, 444, 450, 460, 680, 1038, 1393, 1394, 1396, 1411], "9060": 91, "000709": 91, "nsf": 91, "phy": [91, 275, 284, 313, 371, 372, 383, 385, 434, 573, 1165, 1177, 1182, 1183, 1184, 1187, 1230, 1234, 1287], "0748828": 91, "templeton": 91, "santa": [91, 214, 215, 216, 220], "fe": [91, 214, 215, 216, 220], "under": [91, 324, 325, 525, 535, 555, 566, 577, 586, 588, 606, 671, 672, 673, 674, 739, 1326, 1412, 1413, 1417], "contract": [91, 110, 391, 500, 584, 585, 587, 618, 619, 767, 1174, 1395, 1413], "0340": 91, "space": [92, 101, 109, 231, 296, 301, 302, 308, 309, 355, 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199, 862, 887, 907, 925, 943, 968, 988, 1007, 1404, 1407, 1409, 1410, 1411], "fresh": [165, 862, 907, 943, 988, 1404], "inspir": [165, 230, 231, 342, 681, 862, 907, 943, 988, 1226, 1323, 1404], "deep": [165, 202, 204, 862, 890, 891, 907, 926, 927, 943, 971, 972, 988, 1008, 1009, 1265, 1394], "degreeview": [166, 863, 908, 944, 950, 989, 1404, 1426], "didegreeview": [166, 863], "outedgeview": [168, 189, 467, 468, 613, 747, 750, 865, 878, 1035, 1083, 1404, 1418], "ddict": [168, 176, 184, 189, 865, 870, 873, 878, 910, 916, 946, 951, 955, 960, 991, 998], "in_edg": [168, 189, 865, 878, 946, 960, 1404, 1406, 1407], "out_edg": [168, 865, 946, 1062, 1404, 1406, 1407, 1426], "quietli": [168, 189, 865, 878, 910, 946, 960, 991, 1087, 1426], "outedgedataview": [168, 189, 865, 878, 1404, 1411], "set_data": 169, "edge_dict": [170, 866, 911, 947, 992], "safe": [170, 866, 911, 1404, 1412], "edge_ind": [171, 867, 912, 948, 993], "data_dictionari": [171, 867, 912], "simpler": [172, 184, 868, 873, 913, 916, 949, 955, 994, 998, 1406, 1407, 1417], "indegreeview": [175, 869, 1404], "deg": [175, 188, 243, 259, 356, 361, 685, 869, 877, 950, 959, 1165, 1179, 1222, 1404], "inedgeview": [176, 870, 1404], "inedgedataview": [176, 870], "silent": [180, 193, 195, 320, 871, 882, 884, 914, 921, 923, 952, 963, 965, 995, 1003, 1005, 1085, 1086, 1127, 1353, 1354, 1359, 1363, 1406, 1413], "niter": [180, 681, 682, 683, 684, 851, 871, 896, 914, 932, 952, 977, 995, 1414], "__iter__": [180, 871, 914, 952, 995, 1303], "nodedata": [184, 873, 916, 955, 998], "5pm": [184, 796, 873, 916, 955, 998, 1037, 1039, 1040, 1394, 1426], "Not": [184, 379, 432, 433, 434, 435, 436, 437, 438, 476, 873, 916, 955, 998, 1117, 1216], "nedg": [185, 588, 874, 917, 956, 999], "__len__": [186, 187, 875, 876, 918, 919, 957, 958, 1000, 1001], "outdegreeview": [188, 877], "Will": [193, 362, 605, 607, 610, 882, 921, 963, 1003, 1404, 1414], "get_data": [197, 616], "inplac": [199, 690, 887, 925, 968, 1007, 1066, 1393], "reduct": [199, 469, 618, 786, 887, 925, 968, 1007, 1066, 1320, 1321, 1413, 1414], "sg": [199, 887, 925, 968, 1007], "largest_wcc": [199, 887, 925, 968, 1007], "is_multigraph": [199, 758, 887, 925, 968, 1007, 1154, 1412], "keydict": [199, 207, 887, 892, 925, 928, 968, 973, 1007, 1010, 1039, 1040], "contrast": [202, 204, 301, 302, 308, 309, 890, 891, 926, 927, 971, 972, 1008, 1009, 1066, 1233, 1241, 1426], "reciproc": [204, 299, 320, 322, 356, 411, 430, 447, 476, 620, 758, 891, 972, 1325, 1416, 1425], "mark_half_edg": 206, "li": [206, 619, 670, 675, 685, 775, 1207, 1210, 1425], "straightforward": [207, 892, 928, 973, 1010], "slightli": [207, 326, 437, 520, 521, 581, 892, 928, 973, 1010, 1165, 1326, 1404, 1407, 1412, 1414, 1425], "singleton": [207, 588, 892, 928, 973, 1010, 1218, 1251, 1407], "preserve_attr": [208, 723, 724, 725, 726], "optimum": [208, 231, 583, 720, 722, 791, 1395, 1406], "arboresc": [208, 460, 719, 720, 722, 724, 726, 740, 743, 758, 1272, 1395, 1406], "span": [208, 226, 227, 228, 295, 508, 618, 619, 624, 719, 720, 722, 724, 726, 732, 733, 734, 735, 736, 737, 738, 758, 1394, 1397, 1406, 1407, 1420], "max_ind_cliqu": 209, "networkxnotimpl": [209, 210, 211, 212, 220, 224, 227, 293, 294, 295, 318, 319, 321, 328, 343, 379, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 403, 404, 405, 406, 407, 422, 424, 425, 426, 427, 429, 455, 457, 458, 459, 460, 468, 481, 482, 500, 589, 590, 608, 680, 732, 1043, 1216, 1275, 1276, 1298, 1325, 1353, 1354, 1379, 1407, 1408], "boppana": [209, 211, 212], "halld\u00f3rsson": [209, 211, 212], "1992": [209, 211, 212, 517, 518, 1407], "exclud": [209, 211, 212, 215, 216, 261, 262, 453, 688, 719, 723, 724, 725, 726, 733, 751, 1036, 1038, 1088, 1217, 1412], "180": [209, 211, 212, 238], "196": [209, 211, 212], "heurist": [210, 220, 228, 233, 234, 377, 380, 381, 427, 494, 509, 626, 627, 652, 663, 703, 758, 1173, 1320, 1321, 1325, 1395, 1408, 1412, 1413], "max_cliqu": 210, "rigor": 210, "pattabiraman": 210, "bharath": 210, "massiv": [210, 217], "421": 210, "448": 210, "1080": [210, 297, 298, 306, 307, 329], "15427951": 210, "986778": 210, "apx": [211, 212], "subseteq": [211, 280, 289, 618, 675], "omega": [211, 758, 782, 1414], "maximum_cliqu": 211, "1007": [211, 226, 296, 301, 302, 303, 308, 309, 323, 324, 325, 341, 431, 451, 498, 574, 1144, 1181], "bf01994876": 211, "iset": 212, "trial": [213, 230, 231, 1195, 1237, 1238], "estim": [213, 224, 297, 306, 313, 564, 625, 626, 627, 782, 1280, 1407], "coeffici": [213, 248, 260, 261, 262, 263, 289, 355, 356, 358, 570, 618, 619, 625, 682, 684, 778, 782, 1397, 1398, 1399, 1406, 1413], "fraction": [213, 257, 259, 286, 289, 297, 299, 304, 306, 315, 317, 318, 319, 321, 322, 326, 328, 330, 356, 358, 359, 519, 1165, 1234], "schank": 213, "thoma": [213, 751, 1407, 1409, 1413], "dorothea": [213, 1168], "wagner": [213, 429, 758, 1168, 1402, 1406], "universit\u00e4t": 213, "karlsruh": 213, "fakult\u00e4t": 213, "f\u00fcr": 213, "informatik": [213, 412], "5445": 213, "ir": 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215, 216], "uci": [214, 215, 216, 467, 704, 706, 707, 708, 710, 734, 736], "drwhite": [214, 215, 216], "pprint": [214, 577, 711], "all_pairs_node_connect": [215, 216, 1402, 1424, 1425], "bf": [215, 216, 217, 363, 588, 704, 706, 707, 708, 717, 1397, 1401, 1406, 1409, 1412, 1413], "lose": [215, 796, 1037, 1039, 1040], "accuraci": [215, 312, 786], "platon": [215, 216, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 1245, 1248, 1254, 1257, 1261, 1263], "octahedr": [215, 216, 1257], "approx": [215, 216, 227, 229, 230, 231, 1413], "octahedral_graph": [215, 216], "vari": [217, 238, 243, 373, 378, 569, 695], "sweep": [217, 1412], "dsweep": 217, "a_1": [217, 477], "a_2": 217, "magnien": [217, 260, 261, 262, 289], "cl\u00e9menc": [217, 260, 261, 262, 289], "matthieu": [217, 260, 261, 262, 274, 289], "latapi": [217, 260, 261, 262, 274, 289], "michel": 217, "habib": 217, "empir": 217, "tight": 217, "jea": 217, "0904": 217, "2728": 217, "crescenzi": 217, "pierluigi": 217, "roberto": 217, "grossi": 217, "leonardo": 217, "lanzi": 217, "andrea": [217, 1165, 1413], "marino": 217, "symposium": [217, 619, 1186, 1195, 1239], "berlin": [217, 520, 521, 1413], "heidelberg": [217, 520, 521], "ut": 217, "ee": [217, 313], "mtat": 217, "238": 217, "2014_fall": 217, "domin": [218, 219, 311, 410, 414, 481, 482, 483, 484, 758, 1325, 1395, 1400, 1406, 1407], "opt": [218, 221, 1425], "min_weight_dominating_set": 219, "vazirani": [219, 221], "vijai": [219, 221, 517], "min_dens": 220, "95": [220, 590, 1283, 1284, 1382], "nest": [220, 427, 728, 730, 791, 1038, 1045, 1061, 1094, 1296, 1308, 1348, 1355, 1356, 1357, 1358, 1383, 1406], "forth": [220, 427], "relax": [220, 227, 1171, 1413], "narrow": [220, 1165], "whitnei": 220, "bicompon": [220, 387, 389, 390, 394], "ferraro": [220, 427], "cohes": [220, 427, 437], "1503": [220, 427], "04476v1": [220, 427], "santaf": 220, "ind": 220, "embedded": [220, 305, 427], "sociolog": [220, 427, 748], "103": [220, 427, 1222, 1288, 1292], "2307": [220, 297, 1255], "3088904": 220, "petersen": [220, 427, 761, 1251, 1256, 1259], "triconnect": [220, 427], "apxa": 220, "petersen_graph": [220, 380, 427, 492, 761, 1119, 1120, 1426], "fo": 221, "initial_cut": 222, "highest": [222, 269, 273, 276, 337, 357, 374, 387, 389, 390, 394, 428, 509, 688, 703, 1180], "suppli": [222, 256, 277, 278, 280, 281, 594, 1197, 1320, 1321, 1326, 1345, 1348, 1349, 1350, 1382, 1408, 1413], "cut_valu": [222, 429, 500, 506, 507, 1402], "probabl": [223, 227, 230, 231, 236, 237, 238, 241, 242, 243, 245, 274, 275, 296, 358, 452, 468, 593, 675, 738, 758, 796, 1037, 1039, 1040, 1168, 1169, 1170, 1171, 1173, 1175, 1179, 1182, 1184, 1185, 1186, 1187, 1188, 1193, 1195, 1196, 1197, 1198, 1199, 1203, 1205, 1224, 1225, 1227, 1228, 1229, 1230, 1232, 1233, 1234, 1235, 1236, 1239, 1241, 1278, 1279, 1283, 1284, 1319, 1403, 1404, 1406, 1414, 1417, 1426], "cut_siz": [223, 442, 447, 448, 758], "ramsei": [224, 758], "max_pair": 224, "closur": [225, 226, 467, 468, 1036, 1088, 1395, 1406, 1408, 1411], "terminal_nod": 226, "steiner": [226, 758, 1408, 1425], "leaf": [226, 355, 460, 465, 678, 1155, 1236, 1272], "across": [226, 248, 625, 1038, 1100, 1326, 1405], "kou": 226, "mehlhorn": [226, 511, 512, 1425], "proce": [226, 231, 232, 373, 378, 518, 1165], "steiner_tree_problem": 226, "markowski": 226, "berman": 226, "1981": [226, 1164, 1323], "acta": [226, 508], "informatica": [226, 508], "bf00288961": 226, "kurt": [226, 511, 512], "1988": [226, 1199, 1407], "0020": [226, 455], "0190": [226, 455], "88": [226, 513, 1178, 1180], "90066": 226, "held": [227, 1105], "karp": [227, 277, 278, 280, 499, 758, 1169, 1395, 1402, 1406], "entropi": 227, "scheme": [227, 337, 719, 733, 1393], "lceil": 227, "rceil": 227, "augment": [227, 422, 496, 510, 581, 758, 1408], "tour": [227, 488, 490], "pari": 227, "inequ": [227, 1283, 1284], "trip": [227, 229, 230, 231], "goeman": 227, "madri": 227, "gharan": 227, "saberi": [227, 1181], "1043": 227, "1061": 227, "set_edge_attribut": [227, 374, 500, 598, 626, 1402, 1404, 1407], "minimum_spanning_tre": [228, 1406, 1407], "hamiltonian": [228, 232, 697, 1242, 1244, 1249, 1250, 1254, 1258, 1264], "nico": 228, "rr": 228, "388": [228, 300], "carnegi": 228, "mellon": 228, "univ": 228, "pa": 228, "1976": [228, 453, 516, 1407], "essenc": 229, "feasibl": [229, 422, 494, 496, 498, 499, 502, 503, 504, 505, 508, 509, 510, 531, 534, 541, 544, 762, 1043], "init_cycl": [230, 231, 1413], "temp": [230, 232, 1098], "max_iter": [230, 231, 676], "n_inner": [230, 231], "suboptim": [230, 231, 581], "perturb": [230, 231], "wors": [230, 231, 301, 302, 308, 309, 494], "escap": [230, 231, 1407, 1413], "decreas": [230, 231, 332, 333, 337, 367, 383, 608, 673, 692, 703, 719, 733, 1116, 1175, 1177, 1222, 1234, 1294], "temperatur": [230, 1117], "steel": 230, "harden": 230, "cool": 230, "goe": 230, "greedy_tsp": [230, 231, 232, 1413], "threshold_accepting_tsp": [230, 232, 1413], "transpos": [230, 231, 282], "swap_two_nod": [230, 231], "transposit": [230, 231], "move_one_nod": [230, 231], "enact": [230, 231], "declar": [230, 231], "outer": [230, 231, 380, 436, 606, 615, 796, 1012, 1013, 1018, 1019, 1020, 1021, 1022, 1037, 1039, 1040, 1086, 1160, 1326], "percentag": [230, 231, 1269], "metaheurist": [230, 231], "characterist": [230, 231, 682, 775], "thoughtfulli": [230, 231], "exp": [230, 1197, 1199], "n_i": 230, "n_o": 230, "simulated_ann": 230, "incycl": [230, 231], "amount": [231, 496, 504, 505, 508, 676, 786, 1042, 1296, 1424, 1425], "minima": 231, "slowli": 231, "simulated_annealing_tsp": [231, 232, 1413], "unchang": [231, 1112, 1296], "presenc": [231, 652, 658, 1425], "0021": 231, "9991": 231, "90": [231, 274, 332, 333, 1286], "90201": 231, "asadpour_atsp": [232, 1414], "biggest": 232, "callabl": [232, 525, 535, 545, 552, 553, 554, 555, 671, 672, 673, 674, 796, 1036, 1037, 1039, 1040, 1045, 1046, 1047, 1088, 1102, 1296, 1345, 1348, 1349, 1350, 1406, 1413, 1414, 1425], "tsp": [232, 1413], "curri": 232, "sa_tsp": 232, "wt": [232, 1426], 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"Subgraph": [[118, "subgraph"]], "Harmonic Centrality": [[118, "harmonic-centrality"]], "Dispersion": [[118, "dispersion"]], "Reaching": [[118, "reaching"]], "Percolation": [[118, "percolation"]], "Second Order Centrality": [[118, "second-order-centrality"]], "Trophic": [[118, "trophic"]], "VoteRank": [[118, "voterank"]], "Chains": [[119, "module-networkx.algorithms.chains"]], "Chordal": [[120, "chordal"]], "Coloring": [[123, "module-networkx.algorithms.coloring"]], "Communicability": [[124, "module-networkx.algorithms.communicability_alg"]], "Communities": [[125, "module-networkx.algorithms.community"]], "Bipartitions": [[125, "module-networkx.algorithms.community.kernighan_lin"]], "K-Clique": [[125, "module-networkx.algorithms.community.kclique"]], "Modularity-based communities": [[125, "module-networkx.algorithms.community.modularity_max"]], "Tree partitioning": [[125, "module-networkx.algorithms.community.lukes"]], "Label propagation": [[125, "module-networkx.algorithms.community.label_propagation"]], "Louvain Community Detection": [[125, "module-networkx.algorithms.community.louvain"]], "Fluid Communities": [[125, "module-networkx.algorithms.community.asyn_fluid"]], "Measuring partitions": [[125, "module-networkx.algorithms.community.quality"]], "Partitions via centrality measures": [[125, "module-networkx.algorithms.community.centrality"]], "Validating partitions": [[125, "module-networkx.algorithms.community.community_utils"]], "Components": [[126, "module-networkx.algorithms.components"]], "Strong connectivity": [[126, "strong-connectivity"]], "Weak connectivity": [[126, "weak-connectivity"]], "Attracting components": [[126, "attracting-components"]], "Biconnected components": [[126, "biconnected-components"]], "Semiconnectedness": [[126, "semiconnectedness"]], "Edge-augmentation": [[127, "module-networkx.algorithms.connectivity.edge_augmentation"]], "See Also": [[127, "see-also"], [762, "see-also"], [1041, "see-also"], [1041, "id2"], [1042, "see-also"], [1042, "id3"], [1042, "id5"]], "K-edge-components": [[127, "module-networkx.algorithms.connectivity.edge_kcomponents"]], "K-node-components": [[127, "module-networkx.algorithms.connectivity.kcomponents"]], "K-node-cutsets": [[127, "module-networkx.algorithms.connectivity.kcutsets"]], "Flow-based disjoint paths": [[127, "module-networkx.algorithms.connectivity.disjoint_paths"]], "Flow-based Connectivity": [[127, "module-networkx.algorithms.connectivity.connectivity"]], "Flow-based Minimum Cuts": [[127, "module-networkx.algorithms.connectivity.cuts"]], "Stoer-Wagner minimum cut": [[127, "module-networkx.algorithms.connectivity.stoerwagner"]], "Utils for flow-based connectivity": [[127, "module-networkx.algorithms.connectivity.utils"]], "Cores": [[128, "module-networkx.algorithms.core"]], "Cuts": [[130, "module-networkx.algorithms.cuts"]], "Cycles": [[131, "module-networkx.algorithms.cycles"]], "D-Separation": [[132, "module-networkx.algorithms.d_separation"]], "Blocking paths": [[132, "blocking-paths"]], "Illustration of D-separation with examples": [[132, "illustration-of-d-separation-with-examples"]], "D-separation and its applications in probability": [[132, "d-separation-and-its-applications-in-probability"]], "Examples": [[132, "examples"], [760, "examples"], [762, "examples"], [1041, "examples"], [1041, "id1"], [1042, "examples"], [1042, "id2"], [1042, "id4"], [1387, "examples"], [1393, "examples"], [1394, "examples"], [1402, "examples"], [1406, "examples"], [1406, "id29"], [1406, "id32"], [1406, "id35"], [1406, "id44"], [1406, "id47"], [1406, "id50"], [1406, "id53"], [1406, "id57"], [1406, "id60"], [1406, "id63"], [1406, "id66"], [1406, "id70"], [1406, "id74"]], "Directed Acyclic Graphs": [[133, "module-networkx.algorithms.dag"]], "Distance-Regular Graphs": [[135, "module-networkx.algorithms.distance_regular"]], "Dominance": [[136, "module-networkx.algorithms.dominance"]], "Dominating Sets": [[137, "module-networkx.algorithms.dominating"]], "Efficiency": [[138, "module-networkx.algorithms.efficiency_measures"]], "Eulerian": [[139, "module-networkx.algorithms.euler"]], "Flows": [[140, "module-networkx.algorithms.flow"]], "Maximum Flow": [[140, "maximum-flow"]], "Edmonds-Karp": [[140, "edmonds-karp"]], "Shortest Augmenting Path": [[140, "shortest-augmenting-path"]], "Preflow-Push": [[140, "preflow-push"]], "Dinitz": [[140, "dinitz"]], "Boykov-Kolmogorov": [[140, "boykov-kolmogorov"]], "Gomory-Hu Tree": [[140, "gomory-hu-tree"]], "Utils": [[140, "utils"]], "Network Simplex": [[140, "network-simplex"]], "Capacity Scaling Minimum Cost Flow": [[140, "capacity-scaling-minimum-cost-flow"]], "EdgeComponentAuxGraph.construct": [[141, "edgecomponentauxgraph-construct"]], "EdgeComponentAuxGraph.k_edge_components": [[142, "edgecomponentauxgraph-k-edge-components"]], "EdgeComponentAuxGraph.k_edge_subgraphs": [[143, "edgecomponentauxgraph-k-edge-subgraphs"]], "ISMAGS.analyze_symmetry": [[144, "ismags-analyze-symmetry"]], "ISMAGS.find_isomorphisms": [[145, "ismags-find-isomorphisms"]], "ISMAGS.is_isomorphic": [[146, "ismags-is-isomorphic"]], "ISMAGS.isomorphisms_iter": [[147, "ismags-isomorphisms-iter"]], "ISMAGS.largest_common_subgraph": [[148, "ismags-largest-common-subgraph"]], "ISMAGS.subgraph_is_isomorphic": [[149, "ismags-subgraph-is-isomorphic"]], "ISMAGS.subgraph_isomorphisms_iter": [[150, "ismags-subgraph-isomorphisms-iter"]], "PlanarEmbedding.add_edge": [[151, "planarembedding-add-edge"]], "PlanarEmbedding.add_edges_from": [[152, "planarembedding-add-edges-from"]], "PlanarEmbedding.add_half_edge_ccw": [[153, "planarembedding-add-half-edge-ccw"]], "PlanarEmbedding.add_half_edge_cw": [[154, "planarembedding-add-half-edge-cw"]], "PlanarEmbedding.add_half_edge_first": [[155, "planarembedding-add-half-edge-first"]], "PlanarEmbedding.add_node": [[156, "planarembedding-add-node"]], "PlanarEmbedding.add_nodes_from": [[157, "planarembedding-add-nodes-from"]], "PlanarEmbedding.add_weighted_edges_from": [[158, "planarembedding-add-weighted-edges-from"]], "PlanarEmbedding.adj": [[159, "planarembedding-adj"]], "PlanarEmbedding.adjacency": [[160, "planarembedding-adjacency"]], "PlanarEmbedding.check_structure": [[161, "planarembedding-check-structure"]], "PlanarEmbedding.clear": [[162, "planarembedding-clear"]], "PlanarEmbedding.clear_edges": [[163, "planarembedding-clear-edges"]], "PlanarEmbedding.connect_components": [[164, "planarembedding-connect-components"]], "PlanarEmbedding.copy": [[165, "planarembedding-copy"]], "PlanarEmbedding.degree": [[166, "planarembedding-degree"]], "PlanarEmbedding.edge_subgraph": [[167, "planarembedding-edge-subgraph"]], "PlanarEmbedding.edges": [[168, "planarembedding-edges"]], "PlanarEmbedding.get_data": [[169, "planarembedding-get-data"]], "PlanarEmbedding.get_edge_data": [[170, "planarembedding-get-edge-data"]], "PlanarEmbedding.has_edge": [[171, 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"PlanarEmbedding.succ": [[200, "planarembedding-succ"]], "PlanarEmbedding.successors": [[201, "planarembedding-successors"]], "PlanarEmbedding.to_directed": [[202, "planarembedding-to-directed"]], "PlanarEmbedding.to_directed_class": [[203, "planarembedding-to-directed-class"]], "PlanarEmbedding.to_undirected": [[204, "planarembedding-to-undirected"]], "PlanarEmbedding.to_undirected_class": [[205, "planarembedding-to-undirected-class"]], "PlanarEmbedding.traverse_face": [[206, "planarembedding-traverse-face"]], "PlanarEmbedding.update": [[207, "planarembedding-update"]], "Edmonds.find_optimum": [[208, "edmonds-find-optimum"]], "clique_removal": [[209, "clique-removal"]], "large_clique_size": [[210, "large-clique-size"]], "max_clique": [[211, "max-clique"]], "maximum_independent_set": [[212, "maximum-independent-set"]], "average_clustering": [[213, "average-clustering"], [260, "average-clustering"], [355, "average-clustering"]], "all_pairs_node_connectivity": [[214, 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"node_degree_xy": [[247, "node-degree-xy"]], "numeric_assortativity_coefficient": [[248, "numeric-assortativity-coefficient"]], "find_asteroidal_triple": [[249, "find-asteroidal-triple"]], "is_at_free": [[250, "is-at-free"]], "color": [[251, "color"]], "degrees": [[252, "degrees"]], "density": [[253, "density"], [1060, "density"]], "is_bipartite": [[254, "is-bipartite"]], "is_bipartite_node_set": [[255, "is-bipartite-node-set"]], "sets": [[256, "sets"]], "betweenness_centrality": [[257, "betweenness-centrality"], [297, "betweenness-centrality"]], "closeness_centrality": [[258, "closeness-centrality"], [299, "closeness-centrality"]], "degree_centrality": [[259, "degree-centrality"], [304, "degree-centrality"]], "clustering": [[261, "clustering"], [356, "clustering"]], "latapy_clustering": [[262, "latapy-clustering"]], "robins_alexander_clustering": [[263, "robins-alexander-clustering"]], "min_edge_cover": [[264, "min-edge-cover"], [440, "min-edge-cover"]], "generate_edgelist": [[265, 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"minimum_weight_full_matching": [[280, "minimum-weight-full-matching"]], "to_vertex_cover": [[281, "to-vertex-cover"]], "biadjacency_matrix": [[282, "biadjacency-matrix"]], "from_biadjacency_matrix": [[283, "from-biadjacency-matrix"]], "collaboration_weighted_projected_graph": [[284, "collaboration-weighted-projected-graph"]], "generic_weighted_projected_graph": [[285, "generic-weighted-projected-graph"]], "overlap_weighted_projected_graph": [[286, "overlap-weighted-projected-graph"]], "projected_graph": [[287, "projected-graph"]], "weighted_projected_graph": [[288, "weighted-projected-graph"]], "node_redundancy": [[289, "node-redundancy"]], "spectral_bipartivity": [[290, "spectral-bipartivity"]], "edge_boundary": [[291, "edge-boundary"]], "node_boundary": [[292, "node-boundary"]], "bridges": [[293, "bridges"]], "has_bridges": [[294, "has-bridges"]], "local_bridges": [[295, "local-bridges"]], "approximate_current_flow_betweenness_centrality": [[296, 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"local-reaching-centrality"]], "out_degree_centrality": [[328, "out-degree-centrality"]], "percolation_centrality": [[329, "percolation-centrality"]], "prominent_group": [[330, "prominent-group"]], "second_order_centrality": [[331, "second-order-centrality"]], "subgraph_centrality": [[332, "subgraph-centrality"]], "subgraph_centrality_exp": [[333, "subgraph-centrality-exp"]], "trophic_differences": [[334, "trophic-differences"]], "trophic_incoherence_parameter": [[335, "trophic-incoherence-parameter"]], "trophic_levels": [[336, "trophic-levels"]], "voterank": [[337, "voterank"]], "chain_decomposition": [[338, "chain-decomposition"]], "chordal_graph_cliques": [[339, "chordal-graph-cliques"]], "chordal_graph_treewidth": [[340, "chordal-graph-treewidth"]], "complete_to_chordal_graph": [[341, "complete-to-chordal-graph"]], "find_induced_nodes": [[342, "find-induced-nodes"]], "is_chordal": [[343, "is-chordal"]], "cliques_containing_node": [[344, "cliques-containing-node"]], "enumerate_all_cliques": [[345, "enumerate-all-cliques"]], "find_cliques": [[346, "find-cliques"]], "find_cliques_recursive": [[347, "find-cliques-recursive"]], "graph_clique_number": [[348, "graph-clique-number"]], "graph_number_of_cliques": [[349, "graph-number-of-cliques"]], "make_clique_bipartite": [[350, "make-clique-bipartite"]], "make_max_clique_graph": [[351, "make-max-clique-graph"]], "max_weight_clique": [[352, "max-weight-clique"]], "node_clique_number": [[353, "node-clique-number"]], "number_of_cliques": [[354, "number-of-cliques"]], "generalized_degree": [[357, "generalized-degree"]], "square_clustering": [[358, "square-clustering"]], "transitivity": [[359, "transitivity"]], "triangles": [[360, "triangles"]], "equitable_color": [[361, "equitable-color"]], "greedy_color": [[362, "greedy-color"]], "strategy_connected_sequential": [[363, "strategy-connected-sequential"]], "strategy_connected_sequential_bfs": [[364, "strategy-connected-sequential-bfs"]], 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[[457, "antichains"]], "dag_longest_path": [[458, "dag-longest-path"]], "dag_longest_path_length": [[459, "dag-longest-path-length"]], "dag_to_branching": [[460, "dag-to-branching"]], "descendants": [[461, "descendants"]], "is_aperiodic": [[462, "is-aperiodic"]], "is_directed_acyclic_graph": [[463, "is-directed-acyclic-graph"]], "lexicographical_topological_sort": [[464, "lexicographical-topological-sort"]], "topological_generations": [[465, "topological-generations"]], "topological_sort": [[466, "topological-sort"]], "transitive_closure": [[467, "transitive-closure"]], "transitive_closure_dag": [[468, "transitive-closure-dag"]], "transitive_reduction": [[469, "transitive-reduction"]], "barycenter": [[470, "barycenter"]], "center": [[471, "center"]], "eccentricity": [[473, "eccentricity"]], "periphery": [[474, "periphery"]], "radius": [[475, "radius"]], "resistance_distance": [[476, "resistance-distance"]], "global_parameters": [[477, "global-parameters"]], "intersection_array": [[478, "intersection-array"]], "is_distance_regular": [[479, "is-distance-regular"]], "is_strongly_regular": [[480, "is-strongly-regular"]], "dominance_frontiers": [[481, "dominance-frontiers"]], "immediate_dominators": [[482, "immediate-dominators"]], "dominating_set": [[483, "dominating-set"]], "is_dominating_set": [[484, "is-dominating-set"]], "efficiency": [[485, "efficiency"]], "global_efficiency": [[486, "global-efficiency"]], "local_efficiency": [[487, "local-efficiency"]], "eulerian_circuit": [[488, "eulerian-circuit"]], "eulerian_path": [[489, "eulerian-path"]], "eulerize": [[490, "eulerize"]], "has_eulerian_path": [[491, "has-eulerian-path"]], "is_eulerian": [[492, "is-eulerian"]], "is_semieulerian": [[493, "is-semieulerian"]], "boykov_kolmogorov": [[494, "boykov-kolmogorov"]], "build_residual_network": [[495, "build-residual-network"]], "capacity_scaling": [[496, "capacity-scaling"]], "cost_of_flow": [[497, "cost-of-flow"]], "dinitz": [[498, "dinitz"]], "edmonds_karp": [[499, "edmonds-karp"]], "gomory_hu_tree": [[500, "gomory-hu-tree"]], "max_flow_min_cost": [[501, "max-flow-min-cost"]], "maximum_flow": [[502, "maximum-flow"]], "maximum_flow_value": [[503, "maximum-flow-value"]], "min_cost_flow": [[504, "min-cost-flow"]], "min_cost_flow_cost": [[505, "min-cost-flow-cost"]], "minimum_cut": [[506, "minimum-cut"]], "minimum_cut_value": [[507, "minimum-cut-value"]], "network_simplex": [[508, "network-simplex"]], "preflow_push": [[509, "preflow-push"]], "shortest_augmenting_path": [[510, "shortest-augmenting-path"]], "weisfeiler_lehman_graph_hash": [[511, "weisfeiler-lehman-graph-hash"]], "weisfeiler_lehman_subgraph_hashes": [[512, "weisfeiler-lehman-subgraph-hashes"]], "is_digraphical": [[513, "is-digraphical"]], "is_graphical": [[514, "is-graphical"]], "is_multigraphical": [[515, "is-multigraphical"]], "is_pseudographical": [[516, "is-pseudographical"]], "is_valid_degree_sequence_erdos_gallai": [[517, "is-valid-degree-sequence-erdos-gallai"]], "is_valid_degree_sequence_havel_hakimi": [[518, "is-valid-degree-sequence-havel-hakimi"]], "flow_hierarchy": [[519, "flow-hierarchy"]], "is_kl_connected": [[520, "is-kl-connected"]], "kl_connected_subgraph": [[521, "kl-connected-subgraph"]], "is_isolate": [[522, "is-isolate"]], "isolates": [[523, "isolates"]], "number_of_isolates": [[524, "number-of-isolates"]], "DiGraphMatcher.__init__": [[525, "digraphmatcher-init"]], "DiGraphMatcher.candidate_pairs_iter": [[526, "digraphmatcher-candidate-pairs-iter"]], "DiGraphMatcher.initialize": [[527, "digraphmatcher-initialize"]], "DiGraphMatcher.is_isomorphic": [[528, "digraphmatcher-is-isomorphic"]], "DiGraphMatcher.isomorphisms_iter": [[529, "digraphmatcher-isomorphisms-iter"]], "DiGraphMatcher.match": [[530, "digraphmatcher-match"]], "DiGraphMatcher.semantic_feasibility": [[531, "digraphmatcher-semantic-feasibility"]], "DiGraphMatcher.subgraph_is_isomorphic": [[532, "digraphmatcher-subgraph-is-isomorphic"]], "DiGraphMatcher.subgraph_isomorphisms_iter": [[533, "digraphmatcher-subgraph-isomorphisms-iter"]], "DiGraphMatcher.syntactic_feasibility": [[534, "digraphmatcher-syntactic-feasibility"]], "GraphMatcher.__init__": [[535, "graphmatcher-init"]], "GraphMatcher.candidate_pairs_iter": [[536, "graphmatcher-candidate-pairs-iter"]], "GraphMatcher.initialize": [[537, "graphmatcher-initialize"]], "GraphMatcher.is_isomorphic": [[538, "graphmatcher-is-isomorphic"]], "GraphMatcher.isomorphisms_iter": [[539, "graphmatcher-isomorphisms-iter"]], "GraphMatcher.match": [[540, "graphmatcher-match"]], "GraphMatcher.semantic_feasibility": [[541, "graphmatcher-semantic-feasibility"]], "GraphMatcher.subgraph_is_isomorphic": [[542, "graphmatcher-subgraph-is-isomorphic"]], "GraphMatcher.subgraph_isomorphisms_iter": [[543, "graphmatcher-subgraph-isomorphisms-iter"]], "GraphMatcher.syntactic_feasibility": [[544, "graphmatcher-syntactic-feasibility"]], "networkx.algorithms.isomorphism.ISMAGS": [[545, "networkx-algorithms-isomorphism-ismags"]], "categorical_edge_match": [[546, "categorical-edge-match"]], "categorical_multiedge_match": [[547, "categorical-multiedge-match"]], "categorical_node_match": [[548, "categorical-node-match"]], "could_be_isomorphic": [[549, "could-be-isomorphic"]], "fast_could_be_isomorphic": [[550, "fast-could-be-isomorphic"]], "faster_could_be_isomorphic": [[551, "faster-could-be-isomorphic"]], "generic_edge_match": [[552, "generic-edge-match"]], "generic_multiedge_match": [[553, "generic-multiedge-match"]], "generic_node_match": [[554, "generic-node-match"]], "is_isomorphic": [[555, "is-isomorphic"]], "numerical_edge_match": [[556, "numerical-edge-match"]], "numerical_multiedge_match": [[557, "numerical-multiedge-match"]], "numerical_node_match": [[558, "numerical-node-match"]], "rooted_tree_isomorphism": [[559, "rooted-tree-isomorphism"]], "tree_isomorphism": [[560, "tree-isomorphism"]], "vf2pp_all_isomorphisms": [[561, "vf2pp-all-isomorphisms"]], "vf2pp_is_isomorphic": [[562, "vf2pp-is-isomorphic"]], "vf2pp_isomorphism": [[563, "vf2pp-isomorphism"]], "hits": [[564, "hits"]], "google_matrix": [[565, "google-matrix"]], "pagerank": [[566, "pagerank"]], "adamic_adar_index": [[567, "adamic-adar-index"]], "cn_soundarajan_hopcroft": [[568, "cn-soundarajan-hopcroft"]], "common_neighbor_centrality": [[569, "common-neighbor-centrality"]], "jaccard_coefficient": [[570, "jaccard-coefficient"]], "preferential_attachment": [[571, "preferential-attachment"]], "ra_index_soundarajan_hopcroft": [[572, "ra-index-soundarajan-hopcroft"]], "resource_allocation_index": [[573, "resource-allocation-index"]], "within_inter_cluster": [[574, "within-inter-cluster"]], "all_pairs_lowest_common_ancestor": [[575, "all-pairs-lowest-common-ancestor"]], "lowest_common_ancestor": [[576, "lowest-common-ancestor"]], "tree_all_pairs_lowest_common_ancestor": [[577, "tree-all-pairs-lowest-common-ancestor"]], "is_matching": [[578, "is-matching"]], "is_maximal_matching": [[579, "is-maximal-matching"]], "is_perfect_matching": [[580, "is-perfect-matching"]], "max_weight_matching": [[581, "max-weight-matching"]], "maximal_matching": [[582, "maximal-matching"]], "min_weight_matching": [[583, "min-weight-matching"]], "contracted_edge": [[584, "contracted-edge"]], "contracted_nodes": [[585, "contracted-nodes"]], "equivalence_classes": [[586, "equivalence-classes"]], "identified_nodes": [[587, "identified-nodes"]], "quotient_graph": [[588, "quotient-graph"]], "maximal_independent_set": [[589, "maximal-independent-set"]], "moral_graph": [[590, "moral-graph"]], "harmonic_function": [[591, "harmonic-function"]], "local_and_global_consistency": [[592, "local-and-global-consistency"]], "non_randomness": [[593, "non-randomness"]], "compose_all": [[594, "compose-all"]], "disjoint_union_all": [[595, "disjoint-union-all"]], "intersection_all": [[596, "intersection-all"]], "union_all": [[597, "union-all"]], "compose": [[598, "compose"]], "difference": [[599, "difference"]], "disjoint_union": [[600, "disjoint-union"]], "full_join": [[601, "full-join"]], "intersection": [[602, "intersection"]], "symmetric_difference": [[603, "symmetric-difference"]], "union": [[604, "union"]], "cartesian_product": [[605, "cartesian-product"]], "corona_product": [[606, "corona-product"]], "lexicographic_product": [[607, "lexicographic-product"]], "power": [[608, "power"]], "rooted_product": [[609, "rooted-product"]], "strong_product": [[610, "strong-product"]], "tensor_product": [[611, "tensor-product"]], "complement": [[612, "complement"]], "reverse": [[613, "reverse"]], "combinatorial_embedding_to_pos": [[614, "combinatorial-embedding-to-pos"]], "networkx.algorithms.planarity.PlanarEmbedding": [[615, "networkx-algorithms-planarity-planarembedding"]], "check_planarity": [[616, "check-planarity"]], "is_planar": [[617, "is-planar"]], "chromatic_polynomial": [[618, "chromatic-polynomial"]], "tutte_polynomial": [[619, "tutte-polynomial"]], "overall_reciprocity": [[620, "overall-reciprocity"]], "reciprocity": [[621, "reciprocity"]], "is_k_regular": [[622, "is-k-regular"]], "is_regular": [[623, "is-regular"]], "k_factor": [[624, "k-factor"]], "rich_club_coefficient": [[625, "rich-club-coefficient"]], "astar_path": [[626, "astar-path"]], "astar_path_length": [[627, "astar-path-length"]], "floyd_warshall": [[628, "floyd-warshall"]], "floyd_warshall_numpy": [[629, "floyd-warshall-numpy"]], "floyd_warshall_predecessor_and_distance": [[630, "floyd-warshall-predecessor-and-distance"]], "reconstruct_path": [[631, "reconstruct-path"]], "all_shortest_paths": [[632, "all-shortest-paths"]], "average_shortest_path_length": [[633, "average-shortest-path-length"]], "has_path": [[634, "has-path"]], "shortest_path": [[635, "shortest-path"]], "shortest_path_length": [[636, "shortest-path-length"]], "all_pairs_shortest_path": [[637, "all-pairs-shortest-path"]], "all_pairs_shortest_path_length": [[638, "all-pairs-shortest-path-length"]], "bidirectional_shortest_path": [[639, "bidirectional-shortest-path"]], "predecessor": [[640, "predecessor"]], "single_source_shortest_path": [[641, "single-source-shortest-path"]], "single_source_shortest_path_length": [[642, "single-source-shortest-path-length"]], "single_target_shortest_path": [[643, "single-target-shortest-path"]], "single_target_shortest_path_length": [[644, "single-target-shortest-path-length"]], "all_pairs_bellman_ford_path": [[645, "all-pairs-bellman-ford-path"]], "all_pairs_bellman_ford_path_length": [[646, "all-pairs-bellman-ford-path-length"]], "all_pairs_dijkstra": [[647, "all-pairs-dijkstra"]], "all_pairs_dijkstra_path": [[648, "all-pairs-dijkstra-path"]], "all_pairs_dijkstra_path_length": [[649, "all-pairs-dijkstra-path-length"]], "bellman_ford_path": [[650, "bellman-ford-path"]], "bellman_ford_path_length": [[651, "bellman-ford-path-length"]], "bellman_ford_predecessor_and_distance": [[652, "bellman-ford-predecessor-and-distance"]], "bidirectional_dijkstra": [[653, "bidirectional-dijkstra"]], "dijkstra_path": [[654, "dijkstra-path"]], "dijkstra_path_length": [[655, "dijkstra-path-length"]], "dijkstra_predecessor_and_distance": [[656, "dijkstra-predecessor-and-distance"]], "find_negative_cycle": [[657, "find-negative-cycle"]], "goldberg_radzik": [[658, "goldberg-radzik"]], "johnson": [[659, "johnson"]], "multi_source_dijkstra": [[660, "multi-source-dijkstra"]], "multi_source_dijkstra_path": [[661, "multi-source-dijkstra-path"]], "multi_source_dijkstra_path_length": [[662, "multi-source-dijkstra-path-length"]], "negative_edge_cycle": [[663, "negative-edge-cycle"]], "single_source_bellman_ford": [[664, "single-source-bellman-ford"]], "single_source_bellman_ford_path": [[665, "single-source-bellman-ford-path"]], "single_source_bellman_ford_path_length": [[666, "single-source-bellman-ford-path-length"]], "single_source_dijkstra": [[667, "single-source-dijkstra"]], "single_source_dijkstra_path": [[668, "single-source-dijkstra-path"]], "single_source_dijkstra_path_length": [[669, "single-source-dijkstra-path-length"]], "generate_random_paths": [[670, "generate-random-paths"]], "graph_edit_distance": [[671, "graph-edit-distance"]], "optimal_edit_paths": [[672, "optimal-edit-paths"]], "optimize_edit_paths": [[673, "optimize-edit-paths"]], "optimize_graph_edit_distance": [[674, "optimize-graph-edit-distance"]], "panther_similarity": [[675, "panther-similarity"]], "simrank_similarity": [[676, "simrank-similarity"]], "all_simple_edge_paths": [[677, "all-simple-edge-paths"]], "all_simple_paths": [[678, "all-simple-paths"]], "is_simple_path": [[679, "is-simple-path"]], "shortest_simple_paths": [[680, "shortest-simple-paths"]], "lattice_reference": [[681, "lattice-reference"]], "omega": [[682, "omega"]], "random_reference": [[683, "random-reference"]], "sigma": [[684, "sigma"]], "s_metric": [[685, "s-metric"]], "spanner": [[686, "spanner"]], "constraint": [[687, "constraint"]], "effective_size": [[688, "effective-size"]], "local_constraint": [[689, "local-constraint"]], "dedensify": [[690, "dedensify"]], "snap_aggregation": [[691, "snap-aggregation"]], "connected_double_edge_swap": [[692, "connected-double-edge-swap"]], "directed_edge_swap": [[693, "directed-edge-swap"]], "double_edge_swap": [[694, "double-edge-swap"]], "find_threshold_graph": [[695, "find-threshold-graph"]], "is_threshold_graph": [[696, "is-threshold-graph"]], "hamiltonian_path": [[697, "hamiltonian-path"]], "is_reachable": [[698, "is-reachable"]], "is_tournament": [[700, "is-tournament"]], "random_tournament": [[701, "random-tournament"]], "score_sequence": [[702, "score-sequence"]], "bfs_beam_edges": [[703, "bfs-beam-edges"]], "bfs_edges": [[704, "bfs-edges"]], "bfs_layers": [[705, "bfs-layers"]], "bfs_predecessors": [[706, "bfs-predecessors"]], "bfs_successors": [[707, "bfs-successors"]], "bfs_tree": [[708, "bfs-tree"]], "descendants_at_distance": [[709, "descendants-at-distance"]], "dfs_edges": [[710, "dfs-edges"]], "dfs_labeled_edges": [[711, "dfs-labeled-edges"]], "dfs_postorder_nodes": [[712, "dfs-postorder-nodes"]], "dfs_predecessors": [[713, "dfs-predecessors"]], "dfs_preorder_nodes": [[714, "dfs-preorder-nodes"]], "dfs_successors": [[715, "dfs-successors"]], "dfs_tree": [[716, "dfs-tree"]], "edge_bfs": [[717, "edge-bfs"]], "edge_dfs": [[718, "edge-dfs"]], "networkx.algorithms.tree.branchings.ArborescenceIterator": [[719, "networkx-algorithms-tree-branchings-arborescenceiterator"]], "networkx.algorithms.tree.branchings.Edmonds": [[720, "networkx-algorithms-tree-branchings-edmonds"]], "branching_weight": [[721, "branching-weight"]], "greedy_branching": [[722, "greedy-branching"]], "maximum_branching": [[723, "maximum-branching"]], "maximum_spanning_arborescence": [[724, "maximum-spanning-arborescence"]], "minimum_branching": [[725, "minimum-branching"]], "minimum_spanning_arborescence": [[726, "minimum-spanning-arborescence"]], "NotATree": [[727, "notatree"]], "from_nested_tuple": [[728, "from-nested-tuple"]], "from_prufer_sequence": [[729, "from-prufer-sequence"]], "to_nested_tuple": [[730, "to-nested-tuple"]], "to_prufer_sequence": [[731, "to-prufer-sequence"]], "junction_tree": [[732, "junction-tree"]], "networkx.algorithms.tree.mst.SpanningTreeIterator": [[733, "networkx-algorithms-tree-mst-spanningtreeiterator"]], "maximum_spanning_edges": [[734, "maximum-spanning-edges"]], "maximum_spanning_tree": [[735, "maximum-spanning-tree"]], "minimum_spanning_edges": [[736, "minimum-spanning-edges"]], "minimum_spanning_tree": [[737, "minimum-spanning-tree"]], "random_spanning_tree": [[738, "random-spanning-tree"]], "join": [[739, "join"]], "is_arborescence": [[740, "is-arborescence"]], "is_branching": [[741, "is-branching"]], "is_forest": [[742, "is-forest"]], "is_tree": [[743, "is-tree"]], "all_triads": [[744, "all-triads"]], "all_triplets": [[745, "all-triplets"]], "is_triad": [[746, "is-triad"]], "random_triad": [[747, "random-triad"]], "triad_type": [[748, "triad-type"]], "triadic_census": [[749, "triadic-census"]], "triads_by_type": [[750, "triads-by-type"]], "closeness_vitality": [[751, "closeness-vitality"]], "voronoi_cells": [[752, "voronoi-cells"]], "wiener_index": [[753, "wiener-index"]], "Graph Hashing": [[754, "module-networkx.algorithms.graph_hashing"]], "Graphical degree sequence": [[755, "module-networkx.algorithms.graphical"]], "Hierarchy": [[756, "module-networkx.algorithms.hierarchy"]], "Hybrid": [[757, "module-networkx.algorithms.hybrid"]], "Isolates": [[759, "module-networkx.algorithms.isolate"]], "Isomorphism": [[760, "isomorphism"]], "VF2++": [[760, "module-networkx.algorithms.isomorphism.vf2pp"]], "VF2++ Algorithm": [[760, "vf2-algorithm"]], "Tree Isomorphism": [[760, "module-networkx.algorithms.isomorphism.tree_isomorphism"]], "Advanced Interfaces": [[760, "advanced-interfaces"]], "ISMAGS Algorithm": [[761, "module-networkx.algorithms.isomorphism.ismags"]], "Notes": [[761, "notes"], [762, "notes"]], "ISMAGS object": [[761, "ismags-object"]], "VF2 Algorithm": [[762, "module-networkx.algorithms.isomorphism.isomorphvf2"]], "Subgraph Isomorphism": [[762, "subgraph-isomorphism"]], "Graph Matcher": [[762, "graph-matcher"]], "DiGraph Matcher": [[762, "digraph-matcher"]], "Match helpers": [[762, "match-helpers"]], "Link Analysis": [[763, "link-analysis"]], "PageRank": [[763, "module-networkx.algorithms.link_analysis.pagerank_alg"]], "Hits": [[763, "module-networkx.algorithms.link_analysis.hits_alg"]], "Link Prediction": [[764, "module-networkx.algorithms.link_prediction"]], "Lowest Common Ancestor": [[765, "module-networkx.algorithms.lowest_common_ancestors"]], "Minors": [[767, "module-networkx.algorithms.minors"]], "Maximal independent set": [[768, "module-networkx.algorithms.mis"]], "Moral": [[769, "module-networkx.algorithms.moral"]], "Node Classification": [[770, "module-networkx.algorithms.node_classification"]], "non-randomness": [[771, "module-networkx.algorithms.non_randomness"]], "Operators": [[772, "operators"]], "Planar Drawing": [[773, "module-networkx.algorithms.planar_drawing"]], "Planarity": [[774, "module-networkx.algorithms.planarity"]], "Graph Polynomials": [[775, "module-networkx.algorithms.polynomials"]], "Reciprocity": [[776, "module-networkx.algorithms.reciprocity"]], "Regular": [[777, "module-networkx.algorithms.regular"]], "Rich Club": [[778, "module-networkx.algorithms.richclub"]], "Shortest Paths": [[779, "module-networkx.algorithms.shortest_paths.generic"]], "Advanced Interface": [[779, "module-networkx.algorithms.shortest_paths.unweighted"]], "Dense Graphs": [[779, "module-networkx.algorithms.shortest_paths.dense"]], "A* Algorithm": [[779, "module-networkx.algorithms.shortest_paths.astar"]], "Similarity Measures": [[780, "module-networkx.algorithms.similarity"]], "Simple Paths": [[781, "module-networkx.algorithms.simple_paths"]], "Small-world": [[782, "module-networkx.algorithms.smallworld"]], "s metric": [[783, "module-networkx.algorithms.smetric"]], "Sparsifiers": [[784, "module-networkx.algorithms.sparsifiers"]], "Structural holes": [[785, "module-networkx.algorithms.structuralholes"]], "Summarization": [[786, "module-networkx.algorithms.summarization"]], "Swap": [[787, "module-networkx.algorithms.swap"]], "Threshold Graphs": [[788, "module-networkx.algorithms.threshold"]], "Tournament": [[789, "module-networkx.algorithms.tournament"]], "Traversal": [[790, "traversal"]], "Depth First Search": [[790, "module-networkx.algorithms.traversal.depth_first_search"]], "Breadth First Search": [[790, "module-networkx.algorithms.traversal.breadth_first_search"]], "Beam search": [[790, "module-networkx.algorithms.traversal.beamsearch"]], "Depth First Search on Edges": [[790, "module-networkx.algorithms.traversal.edgedfs"]], "Breadth First Search on Edges": [[790, "module-networkx.algorithms.traversal.edgebfs"]], "Tree": [[791, "tree"]], "Recognition": [[791, 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"FilterMultiInner.get": [[819, "filtermultiinner-get"]], "FilterMultiInner.items": [[820, "filtermultiinner-items"]], "FilterMultiInner.keys": [[821, "filtermultiinner-keys"]], "FilterMultiInner.values": [[822, "filtermultiinner-values"]], "MultiAdjacencyView.copy": [[823, "multiadjacencyview-copy"]], "MultiAdjacencyView.get": [[824, "multiadjacencyview-get"]], "MultiAdjacencyView.items": [[825, "multiadjacencyview-items"]], "MultiAdjacencyView.keys": [[826, "multiadjacencyview-keys"]], "MultiAdjacencyView.values": [[827, "multiadjacencyview-values"]], "UnionAdjacency.copy": [[828, "unionadjacency-copy"]], "UnionAdjacency.get": [[829, "unionadjacency-get"]], "UnionAdjacency.items": [[830, "unionadjacency-items"]], "UnionAdjacency.keys": [[831, "unionadjacency-keys"]], "UnionAdjacency.values": [[832, "unionadjacency-values"]], "UnionAtlas.copy": [[833, "unionatlas-copy"]], "UnionAtlas.get": [[834, "unionatlas-get"]], "UnionAtlas.items": [[835, "unionatlas-items"]], "UnionAtlas.keys": 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"DiGraph.nodes": [[873, "digraph-nodes"]], "DiGraph.number_of_edges": [[874, "digraph-number-of-edges"]], "DiGraph.number_of_nodes": [[875, "digraph-number-of-nodes"]], "DiGraph.order": [[876, "digraph-order"]], "DiGraph.out_degree": [[877, "digraph-out-degree"]], "DiGraph.out_edges": [[878, "digraph-out-edges"]], "DiGraph.pred": [[879, "digraph-pred"]], "DiGraph.predecessors": [[880, "digraph-predecessors"]], "DiGraph.remove_edge": [[881, "digraph-remove-edge"]], "DiGraph.remove_edges_from": [[882, "digraph-remove-edges-from"]], "DiGraph.remove_node": [[883, "digraph-remove-node"]], "DiGraph.remove_nodes_from": [[884, "digraph-remove-nodes-from"]], "DiGraph.reverse": [[885, "digraph-reverse"]], "DiGraph.size": [[886, "digraph-size"]], "DiGraph.subgraph": [[887, "digraph-subgraph"]], "DiGraph.succ": [[888, "digraph-succ"]], "DiGraph.successors": [[889, "digraph-successors"]], "DiGraph.to_directed": [[890, "digraph-to-directed"]], "DiGraph.to_undirected": [[891, 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"MultiDiGraph.__iter__": [[932, "multidigraph-iter"]], "MultiDiGraph.__len__": [[933, "multidigraph-len"]], "MultiDiGraph.add_edge": [[934, "multidigraph-add-edge"]], "MultiDiGraph.add_edges_from": [[935, "multidigraph-add-edges-from"]], "MultiDiGraph.add_node": [[936, "multidigraph-add-node"]], "MultiDiGraph.add_nodes_from": [[937, "multidigraph-add-nodes-from"]], "MultiDiGraph.add_weighted_edges_from": [[938, "multidigraph-add-weighted-edges-from"]], "MultiDiGraph.adj": [[939, "multidigraph-adj"]], "MultiDiGraph.adjacency": [[940, "multidigraph-adjacency"]], "MultiDiGraph.clear": [[941, "multidigraph-clear"]], "MultiDiGraph.clear_edges": [[942, "multidigraph-clear-edges"]], "MultiDiGraph.copy": [[943, "multidigraph-copy"]], "MultiDiGraph.degree": [[944, "multidigraph-degree"]], "MultiDiGraph.edge_subgraph": [[945, "multidigraph-edge-subgraph"]], "MultiDiGraph.edges": [[946, "multidigraph-edges"]], "MultiDiGraph.get_edge_data": [[947, "multidigraph-get-edge-data"]], "MultiDiGraph.has_edge": [[948, "multidigraph-has-edge"]], "MultiDiGraph.has_node": [[949, "multidigraph-has-node"]], "MultiDiGraph.in_degree": [[950, "multidigraph-in-degree"]], "MultiDiGraph.in_edges": [[951, "multidigraph-in-edges"]], "MultiDiGraph.nbunch_iter": [[952, "multidigraph-nbunch-iter"]], "MultiDiGraph.neighbors": [[953, "multidigraph-neighbors"]], "MultiDiGraph.new_edge_key": [[954, "multidigraph-new-edge-key"]], "MultiDiGraph.nodes": [[955, "multidigraph-nodes"]], "MultiDiGraph.number_of_edges": [[956, "multidigraph-number-of-edges"]], "MultiDiGraph.number_of_nodes": [[957, "multidigraph-number-of-nodes"]], "MultiDiGraph.order": [[958, "multidigraph-order"]], "MultiDiGraph.out_degree": [[959, "multidigraph-out-degree"]], "MultiDiGraph.out_edges": [[960, "multidigraph-out-edges"]], "MultiDiGraph.predecessors": [[961, "multidigraph-predecessors"]], "MultiDiGraph.remove_edge": [[962, "multidigraph-remove-edge"]], "MultiDiGraph.remove_edges_from": [[963, 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[[1013, "networkx-classes-coreviews-atlasview"]], "networkx.classes.coreviews.FilterAdjacency": [[1014, "networkx-classes-coreviews-filteradjacency"]], "networkx.classes.coreviews.FilterAtlas": [[1015, "networkx-classes-coreviews-filteratlas"]], "networkx.classes.coreviews.FilterMultiAdjacency": [[1016, "networkx-classes-coreviews-filtermultiadjacency"]], "networkx.classes.coreviews.FilterMultiInner": [[1017, "networkx-classes-coreviews-filtermultiinner"]], "networkx.classes.coreviews.MultiAdjacencyView": [[1018, "networkx-classes-coreviews-multiadjacencyview"]], "networkx.classes.coreviews.UnionAdjacency": [[1019, "networkx-classes-coreviews-unionadjacency"]], "networkx.classes.coreviews.UnionAtlas": [[1020, "networkx-classes-coreviews-unionatlas"]], "networkx.classes.coreviews.UnionMultiAdjacency": [[1021, "networkx-classes-coreviews-unionmultiadjacency"]], "networkx.classes.coreviews.UnionMultiInner": [[1022, "networkx-classes-coreviews-unionmultiinner"]], "hide_diedges": [[1023, "hide-diedges"]], "hide_edges": [[1024, "hide-edges"]], "hide_multidiedges": [[1025, "hide-multidiedges"]], "hide_multiedges": [[1026, "hide-multiedges"]], "hide_nodes": [[1027, "hide-nodes"]], "no_filter": [[1028, "no-filter"]], "show_diedges": [[1029, "show-diedges"]], "show_edges": [[1030, "show-edges"]], "show_multidiedges": [[1031, "show-multidiedges"]], "show_multiedges": [[1032, "show-multiedges"]], "networkx.classes.filters.show_nodes": [[1033, "networkx-classes-filters-show-nodes"]], "generic_graph_view": [[1034, "generic-graph-view"]], "reverse_view": [[1035, "reverse-view"], [1083, "reverse-view"]], "subgraph_view": [[1036, "subgraph-view"], [1088, "subgraph-view"]], "Graph\u2014Undirected graphs with self loops": [[1037, "graph-undirected-graphs-with-self-loops"]], "Graph types": [[1038, "graph-types"]], "Which graph class should I use?": [[1038, "which-graph-class-should-i-use"]], "Basic graph types": [[1038, "basic-graph-types"]], "Graph Views": [[1038, "module-networkx.classes.graphviews"]], "Core Views": [[1038, "module-networkx.classes.coreviews"]], "Filters": [[1038, "filters"]], "Backends": [[1038, "backends"]], "Create a Dispatcher": [[1038, "create-a-dispatcher"]], "MultiDiGraph\u2014Directed graphs with self loops and parallel edges": [[1039, "multidigraph-directed-graphs-with-self-loops-and-parallel-edges"]], "Adding and Removing Nodes and Edges": [[1039, "adding-and-removing-nodes-and-edges"]], "MultiGraph\u2014Undirected graphs with self loops and parallel edges": [[1040, "multigraph-undirected-graphs-with-self-loops-and-parallel-edges"]], "Converting to and from other data formats": [[1041, "converting-to-and-from-other-data-formats"]], "To NetworkX Graph": [[1041, "module-networkx.convert"]], "Dictionaries": [[1041, "dictionaries"]], "Lists": [[1041, "lists"]], "Numpy": [[1041, "module-networkx.convert_matrix"]], "Scipy": [[1041, "scipy"]], "Pandas": [[1041, "pandas"]], "Matplotlib": [[1042, 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Applying classic graph operations, such as:": [[1426, "applying-classic-graph-operations-such-as"]], "2. Using a call to one of the classic small graphs, e.g.,": [[1426, "using-a-call-to-one-of-the-classic-small-graphs-e-g"]], "3. Using a (constructive) generator for a classic graph, e.g.,": [[1426, "using-a-constructive-generator-for-a-classic-graph-e-g"]], "4. Using a stochastic graph generator, e.g,": [[1426, "using-a-stochastic-graph-generator-e-g"]], "5. Reading a graph stored in a file using common graph formats": [[1426, "reading-a-graph-stored-in-a-file-using-common-graph-formats"]], "Analyzing graphs": [[1426, "analyzing-graphs"]], "Drawing graphs": [[1426, "drawing-graphs"]]}, "indexentries": {"module": [[112, "module-networkx.algorithms.approximation"], [112, "module-networkx.algorithms.approximation.clique"], [112, "module-networkx.algorithms.approximation.clustering_coefficient"], [112, "module-networkx.algorithms.approximation.connectivity"], [112, "module-networkx.algorithms.approximation.distance_measures"], [112, "module-networkx.algorithms.approximation.dominating_set"], [112, "module-networkx.algorithms.approximation.kcomponents"], [112, "module-networkx.algorithms.approximation.matching"], [112, "module-networkx.algorithms.approximation.maxcut"], [112, "module-networkx.algorithms.approximation.ramsey"], [112, "module-networkx.algorithms.approximation.steinertree"], [112, 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"module-networkx.algorithms.approximation.clustering_coefficient"]], "networkx.algorithms.approximation.connectivity": [[112, "module-networkx.algorithms.approximation.connectivity"]], "networkx.algorithms.approximation.distance_measures": [[112, "module-networkx.algorithms.approximation.distance_measures"]], "networkx.algorithms.approximation.dominating_set": [[112, "module-networkx.algorithms.approximation.dominating_set"]], "networkx.algorithms.approximation.kcomponents": [[112, "module-networkx.algorithms.approximation.kcomponents"]], "networkx.algorithms.approximation.matching": [[112, "module-networkx.algorithms.approximation.matching"]], "networkx.algorithms.approximation.maxcut": [[112, "module-networkx.algorithms.approximation.maxcut"]], "networkx.algorithms.approximation.ramsey": [[112, "module-networkx.algorithms.approximation.ramsey"]], "networkx.algorithms.approximation.steinertree": [[112, "module-networkx.algorithms.approximation.steinertree"]], "networkx.algorithms.approximation.traveling_salesman": [[112, "module-networkx.algorithms.approximation.traveling_salesman"]], "networkx.algorithms.approximation.treewidth": [[112, "module-networkx.algorithms.approximation.treewidth"]], "networkx.algorithms.approximation.vertex_cover": [[112, "module-networkx.algorithms.approximation.vertex_cover"]], "networkx.algorithms.assortativity": [[113, "module-networkx.algorithms.assortativity"]], "networkx.algorithms.asteroidal": [[114, "module-networkx.algorithms.asteroidal"]], "networkx.algorithms.bipartite": [[115, "module-networkx.algorithms.bipartite"]], "networkx.algorithms.bipartite.basic": [[115, "module-networkx.algorithms.bipartite.basic"]], "networkx.algorithms.bipartite.centrality": [[115, "module-networkx.algorithms.bipartite.centrality"]], "networkx.algorithms.bipartite.cluster": [[115, "module-networkx.algorithms.bipartite.cluster"]], "networkx.algorithms.bipartite.covering": [[115, "module-networkx.algorithms.bipartite.covering"]], "networkx.algorithms.bipartite.edgelist": [[115, "module-networkx.algorithms.bipartite.edgelist"]], "networkx.algorithms.bipartite.generators": [[115, "module-networkx.algorithms.bipartite.generators"]], "networkx.algorithms.bipartite.matching": [[115, "module-networkx.algorithms.bipartite.matching"]], "networkx.algorithms.bipartite.matrix": [[115, "module-networkx.algorithms.bipartite.matrix"]], "networkx.algorithms.bipartite.projection": [[115, "module-networkx.algorithms.bipartite.projection"]], "networkx.algorithms.bipartite.redundancy": [[115, "module-networkx.algorithms.bipartite.redundancy"]], "networkx.algorithms.bipartite.spectral": [[115, "module-networkx.algorithms.bipartite.spectral"]], "networkx.algorithms.boundary": [[116, "module-networkx.algorithms.boundary"]], "networkx.algorithms.bridges": [[117, "module-networkx.algorithms.bridges"]], "networkx.algorithms.centrality": [[118, "module-networkx.algorithms.centrality"]], "networkx.algorithms.chains": [[119, "module-networkx.algorithms.chains"]], "networkx.algorithms.chordal": [[120, "module-networkx.algorithms.chordal"]], "networkx.algorithms.clique": [[121, "module-networkx.algorithms.clique"]], "networkx.algorithms.cluster": [[122, "module-networkx.algorithms.cluster"]], "networkx.algorithms.coloring": [[123, "module-networkx.algorithms.coloring"]], "networkx.algorithms.communicability_alg": [[124, "module-networkx.algorithms.communicability_alg"]], "networkx.algorithms.community": [[125, "module-networkx.algorithms.community"]], "networkx.algorithms.community.asyn_fluid": [[125, "module-networkx.algorithms.community.asyn_fluid"]], "networkx.algorithms.community.centrality": [[125, "module-networkx.algorithms.community.centrality"]], "networkx.algorithms.community.community_utils": [[125, "module-networkx.algorithms.community.community_utils"]], "networkx.algorithms.community.kclique": [[125, "module-networkx.algorithms.community.kclique"]], "networkx.algorithms.community.kernighan_lin": [[125, "module-networkx.algorithms.community.kernighan_lin"]], "networkx.algorithms.community.label_propagation": [[125, "module-networkx.algorithms.community.label_propagation"]], "networkx.algorithms.community.louvain": [[125, "module-networkx.algorithms.community.louvain"]], "networkx.algorithms.community.lukes": [[125, "module-networkx.algorithms.community.lukes"]], "networkx.algorithms.community.modularity_max": [[125, "module-networkx.algorithms.community.modularity_max"]], "networkx.algorithms.community.quality": [[125, "module-networkx.algorithms.community.quality"]], "networkx.algorithms.components": [[126, "module-networkx.algorithms.components"]], "networkx.algorithms.connectivity": [[127, "module-networkx.algorithms.connectivity"]], "networkx.algorithms.connectivity.connectivity": [[127, "module-networkx.algorithms.connectivity.connectivity"]], "networkx.algorithms.connectivity.cuts": [[127, "module-networkx.algorithms.connectivity.cuts"]], "networkx.algorithms.connectivity.disjoint_paths": [[127, "module-networkx.algorithms.connectivity.disjoint_paths"]], "networkx.algorithms.connectivity.edge_augmentation": [[127, "module-networkx.algorithms.connectivity.edge_augmentation"]], "networkx.algorithms.connectivity.edge_kcomponents": [[127, "module-networkx.algorithms.connectivity.edge_kcomponents"]], "networkx.algorithms.connectivity.kcomponents": [[127, "module-networkx.algorithms.connectivity.kcomponents"]], "networkx.algorithms.connectivity.kcutsets": [[127, "module-networkx.algorithms.connectivity.kcutsets"]], "networkx.algorithms.connectivity.stoerwagner": [[127, "module-networkx.algorithms.connectivity.stoerwagner"]], "networkx.algorithms.connectivity.utils": [[127, "module-networkx.algorithms.connectivity.utils"]], "networkx.algorithms.core": [[128, "module-networkx.algorithms.core"]], "networkx.algorithms.covering": [[129, "module-networkx.algorithms.covering"]], "networkx.algorithms.cuts": [[130, "module-networkx.algorithms.cuts"]], "networkx.algorithms.cycles": [[131, "module-networkx.algorithms.cycles"]], "networkx.algorithms.d_separation": [[132, "module-networkx.algorithms.d_separation"]], "networkx.algorithms.dag": [[133, "module-networkx.algorithms.dag"]], "networkx.algorithms.distance_measures": [[134, "module-networkx.algorithms.distance_measures"]], "networkx.algorithms.distance_regular": [[135, "module-networkx.algorithms.distance_regular"]], "networkx.algorithms.dominance": [[136, "module-networkx.algorithms.dominance"]], "networkx.algorithms.dominating": [[137, "module-networkx.algorithms.dominating"]], "networkx.algorithms.efficiency_measures": [[138, "module-networkx.algorithms.efficiency_measures"]], "networkx.algorithms.euler": [[139, "module-networkx.algorithms.euler"]], "networkx.algorithms.flow": [[140, "module-networkx.algorithms.flow"]], "construct() (edgecomponentauxgraph class method)": [[141, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.construct"]], "k_edge_components() (edgecomponentauxgraph method)": [[142, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.k_edge_components"]], "k_edge_subgraphs() (edgecomponentauxgraph method)": [[143, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.k_edge_subgraphs"]], "analyze_symmetry() (ismags method)": [[144, "networkx.algorithms.isomorphism.ISMAGS.analyze_symmetry"]], "find_isomorphisms() (ismags method)": [[145, "networkx.algorithms.isomorphism.ISMAGS.find_isomorphisms"]], "is_isomorphic() (ismags method)": [[146, "networkx.algorithms.isomorphism.ISMAGS.is_isomorphic"]], "isomorphisms_iter() (ismags method)": [[147, "networkx.algorithms.isomorphism.ISMAGS.isomorphisms_iter"]], "largest_common_subgraph() (ismags method)": [[148, "networkx.algorithms.isomorphism.ISMAGS.largest_common_subgraph"]], "subgraph_is_isomorphic() (ismags method)": [[149, "networkx.algorithms.isomorphism.ISMAGS.subgraph_is_isomorphic"]], "subgraph_isomorphisms_iter() (ismags method)": [[150, "networkx.algorithms.isomorphism.ISMAGS.subgraph_isomorphisms_iter"]], "add_edge() (planarembedding method)": [[151, "networkx.algorithms.planarity.PlanarEmbedding.add_edge"]], "add_edges_from() (planarembedding method)": [[152, "networkx.algorithms.planarity.PlanarEmbedding.add_edges_from"]], "add_half_edge_ccw() (planarembedding method)": [[153, "networkx.algorithms.planarity.PlanarEmbedding.add_half_edge_ccw"]], "add_half_edge_cw() (planarembedding method)": [[154, "networkx.algorithms.planarity.PlanarEmbedding.add_half_edge_cw"]], "add_half_edge_first() (planarembedding method)": [[155, "networkx.algorithms.planarity.PlanarEmbedding.add_half_edge_first"]], "add_node() (planarembedding method)": [[156, "networkx.algorithms.planarity.PlanarEmbedding.add_node"]], "add_nodes_from() (planarembedding method)": [[157, "networkx.algorithms.planarity.PlanarEmbedding.add_nodes_from"]], "add_weighted_edges_from() (planarembedding method)": [[158, "networkx.algorithms.planarity.PlanarEmbedding.add_weighted_edges_from"]], "adj (planarembedding property)": [[159, "networkx.algorithms.planarity.PlanarEmbedding.adj"]], "adjacency() (planarembedding method)": [[160, "networkx.algorithms.planarity.PlanarEmbedding.adjacency"]], "check_structure() (planarembedding method)": [[161, "networkx.algorithms.planarity.PlanarEmbedding.check_structure"]], "clear() (planarembedding method)": [[162, "networkx.algorithms.planarity.PlanarEmbedding.clear"]], "clear_edges() (planarembedding method)": [[163, "networkx.algorithms.planarity.PlanarEmbedding.clear_edges"]], "connect_components() (planarembedding method)": [[164, "networkx.algorithms.planarity.PlanarEmbedding.connect_components"]], "copy() (planarembedding method)": [[165, "networkx.algorithms.planarity.PlanarEmbedding.copy"]], "degree (planarembedding property)": [[166, "networkx.algorithms.planarity.PlanarEmbedding.degree"]], "edge_subgraph() (planarembedding method)": [[167, "networkx.algorithms.planarity.PlanarEmbedding.edge_subgraph"]], "edges (planarembedding property)": [[168, "networkx.algorithms.planarity.PlanarEmbedding.edges"]], "get_data() (planarembedding method)": [[169, "networkx.algorithms.planarity.PlanarEmbedding.get_data"]], "get_edge_data() (planarembedding method)": [[170, "networkx.algorithms.planarity.PlanarEmbedding.get_edge_data"]], "has_edge() (planarembedding method)": [[171, "networkx.algorithms.planarity.PlanarEmbedding.has_edge"]], "has_node() (planarembedding method)": [[172, "networkx.algorithms.planarity.PlanarEmbedding.has_node"]], "has_predecessor() (planarembedding method)": [[173, "networkx.algorithms.planarity.PlanarEmbedding.has_predecessor"]], "has_successor() (planarembedding method)": [[174, "networkx.algorithms.planarity.PlanarEmbedding.has_successor"]], "in_degree (planarembedding property)": [[175, "networkx.algorithms.planarity.PlanarEmbedding.in_degree"]], "in_edges (planarembedding property)": [[176, "networkx.algorithms.planarity.PlanarEmbedding.in_edges"]], "is_directed() (planarembedding method)": [[177, "networkx.algorithms.planarity.PlanarEmbedding.is_directed"]], "is_multigraph() (planarembedding method)": [[178, "networkx.algorithms.planarity.PlanarEmbedding.is_multigraph"]], "name (planarembedding property)": [[179, "networkx.algorithms.planarity.PlanarEmbedding.name"]], "nbunch_iter() (planarembedding method)": [[180, "networkx.algorithms.planarity.PlanarEmbedding.nbunch_iter"]], "neighbors() (planarembedding method)": [[181, "networkx.algorithms.planarity.PlanarEmbedding.neighbors"]], "neighbors_cw_order() (planarembedding method)": [[182, "networkx.algorithms.planarity.PlanarEmbedding.neighbors_cw_order"]], "next_face_half_edge() (planarembedding method)": [[183, "networkx.algorithms.planarity.PlanarEmbedding.next_face_half_edge"]], "nodes (planarembedding property)": [[184, "networkx.algorithms.planarity.PlanarEmbedding.nodes"]], "number_of_edges() (planarembedding method)": [[185, "networkx.algorithms.planarity.PlanarEmbedding.number_of_edges"]], "number_of_nodes() (planarembedding method)": [[186, "networkx.algorithms.planarity.PlanarEmbedding.number_of_nodes"]], "order() (planarembedding method)": [[187, "networkx.algorithms.planarity.PlanarEmbedding.order"]], "out_degree (planarembedding property)": [[188, "networkx.algorithms.planarity.PlanarEmbedding.out_degree"]], "out_edges (planarembedding property)": [[189, "networkx.algorithms.planarity.PlanarEmbedding.out_edges"]], "pred (planarembedding property)": [[190, "networkx.algorithms.planarity.PlanarEmbedding.pred"]], "predecessors() (planarembedding method)": [[191, "networkx.algorithms.planarity.PlanarEmbedding.predecessors"]], "remove_edge() (planarembedding method)": [[192, "networkx.algorithms.planarity.PlanarEmbedding.remove_edge"]], "remove_edges_from() (planarembedding method)": [[193, "networkx.algorithms.planarity.PlanarEmbedding.remove_edges_from"]], "remove_node() (planarembedding method)": [[194, "networkx.algorithms.planarity.PlanarEmbedding.remove_node"]], "remove_nodes_from() (planarembedding method)": [[195, "networkx.algorithms.planarity.PlanarEmbedding.remove_nodes_from"]], "reverse() (planarembedding method)": [[196, "networkx.algorithms.planarity.PlanarEmbedding.reverse"]], "set_data() (planarembedding method)": [[197, "networkx.algorithms.planarity.PlanarEmbedding.set_data"]], "size() (planarembedding method)": [[198, "networkx.algorithms.planarity.PlanarEmbedding.size"]], "subgraph() (planarembedding method)": [[199, "networkx.algorithms.planarity.PlanarEmbedding.subgraph"]], "succ (planarembedding property)": [[200, "networkx.algorithms.planarity.PlanarEmbedding.succ"]], "successors() (planarembedding method)": [[201, "networkx.algorithms.planarity.PlanarEmbedding.successors"]], "to_directed() (planarembedding method)": [[202, "networkx.algorithms.planarity.PlanarEmbedding.to_directed"]], "to_directed_class() (planarembedding method)": [[203, "networkx.algorithms.planarity.PlanarEmbedding.to_directed_class"]], "to_undirected() (planarembedding method)": [[204, "networkx.algorithms.planarity.PlanarEmbedding.to_undirected"]], "to_undirected_class() (planarembedding method)": [[205, "networkx.algorithms.planarity.PlanarEmbedding.to_undirected_class"]], "traverse_face() (planarembedding method)": [[206, "networkx.algorithms.planarity.PlanarEmbedding.traverse_face"]], "update() (planarembedding method)": [[207, "networkx.algorithms.planarity.PlanarEmbedding.update"]], "find_optimum() (edmonds method)": [[208, "networkx.algorithms.tree.branchings.Edmonds.find_optimum"]], "clique_removal() (in module networkx.algorithms.approximation.clique)": [[209, "networkx.algorithms.approximation.clique.clique_removal"]], "large_clique_size() (in module networkx.algorithms.approximation.clique)": [[210, "networkx.algorithms.approximation.clique.large_clique_size"]], "max_clique() (in module networkx.algorithms.approximation.clique)": [[211, "networkx.algorithms.approximation.clique.max_clique"]], "maximum_independent_set() (in module networkx.algorithms.approximation.clique)": [[212, "networkx.algorithms.approximation.clique.maximum_independent_set"]], "average_clustering() (in module networkx.algorithms.approximation.clustering_coefficient)": [[213, "networkx.algorithms.approximation.clustering_coefficient.average_clustering"]], "all_pairs_node_connectivity() (in module networkx.algorithms.approximation.connectivity)": [[214, "networkx.algorithms.approximation.connectivity.all_pairs_node_connectivity"]], "local_node_connectivity() (in module networkx.algorithms.approximation.connectivity)": [[215, "networkx.algorithms.approximation.connectivity.local_node_connectivity"]], "node_connectivity() (in module networkx.algorithms.approximation.connectivity)": [[216, "networkx.algorithms.approximation.connectivity.node_connectivity"]], "diameter() (in module networkx.algorithms.approximation.distance_measures)": [[217, "networkx.algorithms.approximation.distance_measures.diameter"]], "min_edge_dominating_set() (in module networkx.algorithms.approximation.dominating_set)": [[218, "networkx.algorithms.approximation.dominating_set.min_edge_dominating_set"]], "min_weighted_dominating_set() (in module networkx.algorithms.approximation.dominating_set)": [[219, "networkx.algorithms.approximation.dominating_set.min_weighted_dominating_set"]], "k_components() (in module networkx.algorithms.approximation.kcomponents)": [[220, "networkx.algorithms.approximation.kcomponents.k_components"]], "min_maximal_matching() (in module networkx.algorithms.approximation.matching)": [[221, "networkx.algorithms.approximation.matching.min_maximal_matching"]], "one_exchange() (in module networkx.algorithms.approximation.maxcut)": [[222, "networkx.algorithms.approximation.maxcut.one_exchange"]], "randomized_partitioning() (in module networkx.algorithms.approximation.maxcut)": [[223, "networkx.algorithms.approximation.maxcut.randomized_partitioning"]], "ramsey_r2() (in module networkx.algorithms.approximation.ramsey)": [[224, "networkx.algorithms.approximation.ramsey.ramsey_R2"]], "metric_closure() (in module networkx.algorithms.approximation.steinertree)": [[225, "networkx.algorithms.approximation.steinertree.metric_closure"]], "steiner_tree() (in module networkx.algorithms.approximation.steinertree)": [[226, "networkx.algorithms.approximation.steinertree.steiner_tree"]], "asadpour_atsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[227, "networkx.algorithms.approximation.traveling_salesman.asadpour_atsp"]], "christofides() (in module networkx.algorithms.approximation.traveling_salesman)": [[228, "networkx.algorithms.approximation.traveling_salesman.christofides"]], "greedy_tsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[229, "networkx.algorithms.approximation.traveling_salesman.greedy_tsp"]], "simulated_annealing_tsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[230, "networkx.algorithms.approximation.traveling_salesman.simulated_annealing_tsp"]], "threshold_accepting_tsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[231, "networkx.algorithms.approximation.traveling_salesman.threshold_accepting_tsp"]], "traveling_salesman_problem() (in module networkx.algorithms.approximation.traveling_salesman)": [[232, "networkx.algorithms.approximation.traveling_salesman.traveling_salesman_problem"]], "treewidth_min_degree() (in module networkx.algorithms.approximation.treewidth)": [[233, "networkx.algorithms.approximation.treewidth.treewidth_min_degree"]], "treewidth_min_fill_in() (in module networkx.algorithms.approximation.treewidth)": [[234, "networkx.algorithms.approximation.treewidth.treewidth_min_fill_in"]], "min_weighted_vertex_cover() (in module networkx.algorithms.approximation.vertex_cover)": [[235, "networkx.algorithms.approximation.vertex_cover.min_weighted_vertex_cover"]], "attribute_assortativity_coefficient() (in module networkx.algorithms.assortativity)": [[236, "networkx.algorithms.assortativity.attribute_assortativity_coefficient"]], "attribute_mixing_dict() (in module networkx.algorithms.assortativity)": [[237, "networkx.algorithms.assortativity.attribute_mixing_dict"]], "attribute_mixing_matrix() (in module networkx.algorithms.assortativity)": [[238, "networkx.algorithms.assortativity.attribute_mixing_matrix"]], "average_degree_connectivity() (in module networkx.algorithms.assortativity)": [[239, "networkx.algorithms.assortativity.average_degree_connectivity"]], "average_neighbor_degree() (in module networkx.algorithms.assortativity)": [[240, "networkx.algorithms.assortativity.average_neighbor_degree"]], "degree_assortativity_coefficient() (in module networkx.algorithms.assortativity)": [[241, "networkx.algorithms.assortativity.degree_assortativity_coefficient"]], "degree_mixing_dict() (in module networkx.algorithms.assortativity)": [[242, "networkx.algorithms.assortativity.degree_mixing_dict"]], "degree_mixing_matrix() (in module networkx.algorithms.assortativity)": [[243, "networkx.algorithms.assortativity.degree_mixing_matrix"]], "degree_pearson_correlation_coefficient() (in module networkx.algorithms.assortativity)": [[244, "networkx.algorithms.assortativity.degree_pearson_correlation_coefficient"]], "mixing_dict() (in module networkx.algorithms.assortativity)": [[245, "networkx.algorithms.assortativity.mixing_dict"]], "node_attribute_xy() (in module networkx.algorithms.assortativity)": [[246, "networkx.algorithms.assortativity.node_attribute_xy"]], "node_degree_xy() (in module networkx.algorithms.assortativity)": [[247, "networkx.algorithms.assortativity.node_degree_xy"]], "numeric_assortativity_coefficient() (in module networkx.algorithms.assortativity)": [[248, "networkx.algorithms.assortativity.numeric_assortativity_coefficient"]], "find_asteroidal_triple() (in module networkx.algorithms.asteroidal)": [[249, "networkx.algorithms.asteroidal.find_asteroidal_triple"]], "is_at_free() (in module networkx.algorithms.asteroidal)": [[250, "networkx.algorithms.asteroidal.is_at_free"]], "color() (in module networkx.algorithms.bipartite.basic)": [[251, "networkx.algorithms.bipartite.basic.color"]], "degrees() (in module networkx.algorithms.bipartite.basic)": [[252, "networkx.algorithms.bipartite.basic.degrees"]], "density() (in module networkx.algorithms.bipartite.basic)": [[253, "networkx.algorithms.bipartite.basic.density"]], "is_bipartite() (in module networkx.algorithms.bipartite.basic)": [[254, "networkx.algorithms.bipartite.basic.is_bipartite"]], "is_bipartite_node_set() (in module networkx.algorithms.bipartite.basic)": [[255, "networkx.algorithms.bipartite.basic.is_bipartite_node_set"]], "sets() (in module networkx.algorithms.bipartite.basic)": [[256, "networkx.algorithms.bipartite.basic.sets"]], "betweenness_centrality() (in module networkx.algorithms.bipartite.centrality)": [[257, "networkx.algorithms.bipartite.centrality.betweenness_centrality"]], "closeness_centrality() (in module networkx.algorithms.bipartite.centrality)": [[258, "networkx.algorithms.bipartite.centrality.closeness_centrality"]], "degree_centrality() (in module networkx.algorithms.bipartite.centrality)": [[259, "networkx.algorithms.bipartite.centrality.degree_centrality"]], "average_clustering() (in module networkx.algorithms.bipartite.cluster)": [[260, "networkx.algorithms.bipartite.cluster.average_clustering"]], "clustering() (in module networkx.algorithms.bipartite.cluster)": [[261, "networkx.algorithms.bipartite.cluster.clustering"]], "latapy_clustering() (in module networkx.algorithms.bipartite.cluster)": [[262, "networkx.algorithms.bipartite.cluster.latapy_clustering"]], "robins_alexander_clustering() (in module networkx.algorithms.bipartite.cluster)": [[263, "networkx.algorithms.bipartite.cluster.robins_alexander_clustering"]], "min_edge_cover() (in module networkx.algorithms.bipartite.covering)": [[264, "networkx.algorithms.bipartite.covering.min_edge_cover"]], "generate_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[265, "networkx.algorithms.bipartite.edgelist.generate_edgelist"]], "parse_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[266, "networkx.algorithms.bipartite.edgelist.parse_edgelist"]], "read_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[267, "networkx.algorithms.bipartite.edgelist.read_edgelist"]], "write_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[268, "networkx.algorithms.bipartite.edgelist.write_edgelist"]], "alternating_havel_hakimi_graph() (in module networkx.algorithms.bipartite.generators)": [[269, "networkx.algorithms.bipartite.generators.alternating_havel_hakimi_graph"]], "complete_bipartite_graph() (in module networkx.algorithms.bipartite.generators)": [[270, "networkx.algorithms.bipartite.generators.complete_bipartite_graph"]], "configuration_model() (in module networkx.algorithms.bipartite.generators)": [[271, "networkx.algorithms.bipartite.generators.configuration_model"]], "gnmk_random_graph() (in module networkx.algorithms.bipartite.generators)": [[272, "networkx.algorithms.bipartite.generators.gnmk_random_graph"]], "havel_hakimi_graph() (in module networkx.algorithms.bipartite.generators)": [[273, "networkx.algorithms.bipartite.generators.havel_hakimi_graph"]], "preferential_attachment_graph() (in module networkx.algorithms.bipartite.generators)": [[274, "networkx.algorithms.bipartite.generators.preferential_attachment_graph"]], "random_graph() (in module networkx.algorithms.bipartite.generators)": [[275, "networkx.algorithms.bipartite.generators.random_graph"]], "reverse_havel_hakimi_graph() (in module networkx.algorithms.bipartite.generators)": [[276, "networkx.algorithms.bipartite.generators.reverse_havel_hakimi_graph"]], "eppstein_matching() (in module networkx.algorithms.bipartite.matching)": [[277, "networkx.algorithms.bipartite.matching.eppstein_matching"]], "hopcroft_karp_matching() (in module networkx.algorithms.bipartite.matching)": [[278, "networkx.algorithms.bipartite.matching.hopcroft_karp_matching"]], "maximum_matching() (in module networkx.algorithms.bipartite.matching)": [[279, "networkx.algorithms.bipartite.matching.maximum_matching"]], "minimum_weight_full_matching() (in module networkx.algorithms.bipartite.matching)": [[280, "networkx.algorithms.bipartite.matching.minimum_weight_full_matching"]], "to_vertex_cover() (in module networkx.algorithms.bipartite.matching)": [[281, "networkx.algorithms.bipartite.matching.to_vertex_cover"]], "biadjacency_matrix() (in module networkx.algorithms.bipartite.matrix)": [[282, "networkx.algorithms.bipartite.matrix.biadjacency_matrix"]], "from_biadjacency_matrix() (in module networkx.algorithms.bipartite.matrix)": [[283, "networkx.algorithms.bipartite.matrix.from_biadjacency_matrix"]], "collaboration_weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[284, "networkx.algorithms.bipartite.projection.collaboration_weighted_projected_graph"]], "generic_weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[285, "networkx.algorithms.bipartite.projection.generic_weighted_projected_graph"]], "overlap_weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[286, "networkx.algorithms.bipartite.projection.overlap_weighted_projected_graph"]], "projected_graph() (in module networkx.algorithms.bipartite.projection)": [[287, "networkx.algorithms.bipartite.projection.projected_graph"]], "weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[288, "networkx.algorithms.bipartite.projection.weighted_projected_graph"]], "node_redundancy() (in module networkx.algorithms.bipartite.redundancy)": [[289, "networkx.algorithms.bipartite.redundancy.node_redundancy"]], "spectral_bipartivity() (in module networkx.algorithms.bipartite.spectral)": [[290, "networkx.algorithms.bipartite.spectral.spectral_bipartivity"]], "edge_boundary() (in module networkx.algorithms.boundary)": [[291, "networkx.algorithms.boundary.edge_boundary"]], "node_boundary() (in module networkx.algorithms.boundary)": [[292, "networkx.algorithms.boundary.node_boundary"]], "bridges() (in module networkx.algorithms.bridges)": [[293, "networkx.algorithms.bridges.bridges"]], "has_bridges() (in module networkx.algorithms.bridges)": [[294, "networkx.algorithms.bridges.has_bridges"]], "local_bridges() (in module networkx.algorithms.bridges)": [[295, "networkx.algorithms.bridges.local_bridges"]], "approximate_current_flow_betweenness_centrality() (in module networkx.algorithms.centrality)": [[296, "networkx.algorithms.centrality.approximate_current_flow_betweenness_centrality"]], "betweenness_centrality() (in module networkx.algorithms.centrality)": [[297, "networkx.algorithms.centrality.betweenness_centrality"]], "betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[298, "networkx.algorithms.centrality.betweenness_centrality_subset"]], "closeness_centrality() (in module networkx.algorithms.centrality)": [[299, "networkx.algorithms.centrality.closeness_centrality"]], "communicability_betweenness_centrality() (in module networkx.algorithms.centrality)": [[300, "networkx.algorithms.centrality.communicability_betweenness_centrality"]], "current_flow_betweenness_centrality() (in module networkx.algorithms.centrality)": [[301, "networkx.algorithms.centrality.current_flow_betweenness_centrality"]], "current_flow_betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[302, "networkx.algorithms.centrality.current_flow_betweenness_centrality_subset"]], "current_flow_closeness_centrality() (in module networkx.algorithms.centrality)": [[303, "networkx.algorithms.centrality.current_flow_closeness_centrality"]], "degree_centrality() (in module networkx.algorithms.centrality)": [[304, "networkx.algorithms.centrality.degree_centrality"]], "dispersion() (in module networkx.algorithms.centrality)": [[305, "networkx.algorithms.centrality.dispersion"]], "edge_betweenness_centrality() (in module networkx.algorithms.centrality)": [[306, "networkx.algorithms.centrality.edge_betweenness_centrality"]], "edge_betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[307, "networkx.algorithms.centrality.edge_betweenness_centrality_subset"]], "edge_current_flow_betweenness_centrality() (in module networkx.algorithms.centrality)": [[308, "networkx.algorithms.centrality.edge_current_flow_betweenness_centrality"]], "edge_current_flow_betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[309, "networkx.algorithms.centrality.edge_current_flow_betweenness_centrality_subset"]], "edge_load_centrality() (in module networkx.algorithms.centrality)": [[310, "networkx.algorithms.centrality.edge_load_centrality"]], "eigenvector_centrality() (in module networkx.algorithms.centrality)": [[311, "networkx.algorithms.centrality.eigenvector_centrality"]], "eigenvector_centrality_numpy() (in module networkx.algorithms.centrality)": [[312, "networkx.algorithms.centrality.eigenvector_centrality_numpy"]], "estrada_index() (in module networkx.algorithms.centrality)": [[313, "networkx.algorithms.centrality.estrada_index"]], "global_reaching_centrality() (in module networkx.algorithms.centrality)": [[314, "networkx.algorithms.centrality.global_reaching_centrality"]], "group_betweenness_centrality() (in module networkx.algorithms.centrality)": [[315, "networkx.algorithms.centrality.group_betweenness_centrality"]], "group_closeness_centrality() (in module networkx.algorithms.centrality)": [[316, "networkx.algorithms.centrality.group_closeness_centrality"]], "group_degree_centrality() (in module networkx.algorithms.centrality)": [[317, "networkx.algorithms.centrality.group_degree_centrality"]], "group_in_degree_centrality() (in module networkx.algorithms.centrality)": [[318, "networkx.algorithms.centrality.group_in_degree_centrality"]], "group_out_degree_centrality() (in module networkx.algorithms.centrality)": [[319, "networkx.algorithms.centrality.group_out_degree_centrality"]], "harmonic_centrality() (in module networkx.algorithms.centrality)": [[320, "networkx.algorithms.centrality.harmonic_centrality"]], "in_degree_centrality() (in module networkx.algorithms.centrality)": [[321, "networkx.algorithms.centrality.in_degree_centrality"]], "incremental_closeness_centrality() (in module networkx.algorithms.centrality)": [[322, "networkx.algorithms.centrality.incremental_closeness_centrality"]], "information_centrality() (in module networkx.algorithms.centrality)": [[323, "networkx.algorithms.centrality.information_centrality"]], "katz_centrality() (in module networkx.algorithms.centrality)": [[324, "networkx.algorithms.centrality.katz_centrality"]], "katz_centrality_numpy() (in module networkx.algorithms.centrality)": [[325, "networkx.algorithms.centrality.katz_centrality_numpy"]], "load_centrality() (in module networkx.algorithms.centrality)": [[326, "networkx.algorithms.centrality.load_centrality"]], "local_reaching_centrality() (in module networkx.algorithms.centrality)": [[327, "networkx.algorithms.centrality.local_reaching_centrality"]], "out_degree_centrality() (in module networkx.algorithms.centrality)": [[328, "networkx.algorithms.centrality.out_degree_centrality"]], "percolation_centrality() (in module networkx.algorithms.centrality)": [[329, "networkx.algorithms.centrality.percolation_centrality"]], "prominent_group() (in module networkx.algorithms.centrality)": [[330, "networkx.algorithms.centrality.prominent_group"]], "second_order_centrality() (in module networkx.algorithms.centrality)": [[331, "networkx.algorithms.centrality.second_order_centrality"]], "subgraph_centrality() (in module networkx.algorithms.centrality)": [[332, "networkx.algorithms.centrality.subgraph_centrality"]], "subgraph_centrality_exp() (in module networkx.algorithms.centrality)": [[333, "networkx.algorithms.centrality.subgraph_centrality_exp"]], "trophic_differences() (in module networkx.algorithms.centrality)": [[334, "networkx.algorithms.centrality.trophic_differences"]], "trophic_incoherence_parameter() (in module networkx.algorithms.centrality)": [[335, "networkx.algorithms.centrality.trophic_incoherence_parameter"]], "trophic_levels() (in module networkx.algorithms.centrality)": [[336, "networkx.algorithms.centrality.trophic_levels"]], "voterank() (in module networkx.algorithms.centrality)": [[337, "networkx.algorithms.centrality.voterank"]], "chain_decomposition() (in module networkx.algorithms.chains)": [[338, "networkx.algorithms.chains.chain_decomposition"]], "chordal_graph_cliques() (in module networkx.algorithms.chordal)": [[339, "networkx.algorithms.chordal.chordal_graph_cliques"]], "chordal_graph_treewidth() (in module networkx.algorithms.chordal)": [[340, "networkx.algorithms.chordal.chordal_graph_treewidth"]], "complete_to_chordal_graph() (in module networkx.algorithms.chordal)": [[341, "networkx.algorithms.chordal.complete_to_chordal_graph"]], "find_induced_nodes() (in module networkx.algorithms.chordal)": [[342, "networkx.algorithms.chordal.find_induced_nodes"]], "is_chordal() (in module networkx.algorithms.chordal)": [[343, "networkx.algorithms.chordal.is_chordal"]], "cliques_containing_node() (in module networkx.algorithms.clique)": [[344, "networkx.algorithms.clique.cliques_containing_node"]], "enumerate_all_cliques() (in module networkx.algorithms.clique)": [[345, "networkx.algorithms.clique.enumerate_all_cliques"]], "find_cliques() (in module networkx.algorithms.clique)": [[346, "networkx.algorithms.clique.find_cliques"]], "find_cliques_recursive() (in module networkx.algorithms.clique)": [[347, "networkx.algorithms.clique.find_cliques_recursive"]], "graph_clique_number() (in module networkx.algorithms.clique)": [[348, "networkx.algorithms.clique.graph_clique_number"]], "graph_number_of_cliques() (in module networkx.algorithms.clique)": [[349, "networkx.algorithms.clique.graph_number_of_cliques"]], "make_clique_bipartite() (in module networkx.algorithms.clique)": [[350, "networkx.algorithms.clique.make_clique_bipartite"]], "make_max_clique_graph() (in module networkx.algorithms.clique)": [[351, "networkx.algorithms.clique.make_max_clique_graph"]], "max_weight_clique() (in module networkx.algorithms.clique)": [[352, "networkx.algorithms.clique.max_weight_clique"]], "node_clique_number() (in module networkx.algorithms.clique)": [[353, "networkx.algorithms.clique.node_clique_number"]], "number_of_cliques() (in module networkx.algorithms.clique)": [[354, "networkx.algorithms.clique.number_of_cliques"]], "average_clustering() (in module networkx.algorithms.cluster)": [[355, "networkx.algorithms.cluster.average_clustering"]], "clustering() (in module networkx.algorithms.cluster)": [[356, "networkx.algorithms.cluster.clustering"]], "generalized_degree() (in module networkx.algorithms.cluster)": [[357, "networkx.algorithms.cluster.generalized_degree"]], "square_clustering() (in module networkx.algorithms.cluster)": [[358, "networkx.algorithms.cluster.square_clustering"]], "transitivity() (in module networkx.algorithms.cluster)": [[359, "networkx.algorithms.cluster.transitivity"]], "triangles() (in module networkx.algorithms.cluster)": [[360, "networkx.algorithms.cluster.triangles"]], "equitable_color() (in module networkx.algorithms.coloring)": [[361, "networkx.algorithms.coloring.equitable_color"]], "greedy_color() (in module networkx.algorithms.coloring)": [[362, "networkx.algorithms.coloring.greedy_color"]], "strategy_connected_sequential() (in module networkx.algorithms.coloring)": [[363, "networkx.algorithms.coloring.strategy_connected_sequential"]], "strategy_connected_sequential_bfs() (in module networkx.algorithms.coloring)": [[364, "networkx.algorithms.coloring.strategy_connected_sequential_bfs"]], "strategy_connected_sequential_dfs() (in module networkx.algorithms.coloring)": [[365, "networkx.algorithms.coloring.strategy_connected_sequential_dfs"]], "strategy_independent_set() (in module networkx.algorithms.coloring)": [[366, "networkx.algorithms.coloring.strategy_independent_set"]], "strategy_largest_first() (in module networkx.algorithms.coloring)": [[367, "networkx.algorithms.coloring.strategy_largest_first"]], "strategy_random_sequential() (in module networkx.algorithms.coloring)": [[368, "networkx.algorithms.coloring.strategy_random_sequential"]], "strategy_saturation_largest_first() (in module networkx.algorithms.coloring)": [[369, "networkx.algorithms.coloring.strategy_saturation_largest_first"]], "strategy_smallest_last() (in module networkx.algorithms.coloring)": [[370, "networkx.algorithms.coloring.strategy_smallest_last"]], "communicability() (in module networkx.algorithms.communicability_alg)": [[371, "networkx.algorithms.communicability_alg.communicability"]], "communicability_exp() (in module networkx.algorithms.communicability_alg)": [[372, "networkx.algorithms.communicability_alg.communicability_exp"]], "asyn_fluidc() (in module networkx.algorithms.community.asyn_fluid)": [[373, "networkx.algorithms.community.asyn_fluid.asyn_fluidc"]], "girvan_newman() (in module networkx.algorithms.community.centrality)": [[374, "networkx.algorithms.community.centrality.girvan_newman"]], "is_partition() (in module networkx.algorithms.community.community_utils)": [[375, "networkx.algorithms.community.community_utils.is_partition"]], "k_clique_communities() (in module networkx.algorithms.community.kclique)": [[376, "networkx.algorithms.community.kclique.k_clique_communities"]], "kernighan_lin_bisection() (in module networkx.algorithms.community.kernighan_lin)": [[377, "networkx.algorithms.community.kernighan_lin.kernighan_lin_bisection"]], "asyn_lpa_communities() (in module networkx.algorithms.community.label_propagation)": [[378, "networkx.algorithms.community.label_propagation.asyn_lpa_communities"]], "label_propagation_communities() (in module networkx.algorithms.community.label_propagation)": [[379, "networkx.algorithms.community.label_propagation.label_propagation_communities"]], "louvain_communities() (in module networkx.algorithms.community.louvain)": [[380, "networkx.algorithms.community.louvain.louvain_communities"]], "louvain_partitions() (in module networkx.algorithms.community.louvain)": [[381, "networkx.algorithms.community.louvain.louvain_partitions"]], "lukes_partitioning() (in module networkx.algorithms.community.lukes)": [[382, "networkx.algorithms.community.lukes.lukes_partitioning"]], "greedy_modularity_communities() (in module networkx.algorithms.community.modularity_max)": [[383, "networkx.algorithms.community.modularity_max.greedy_modularity_communities"]], "naive_greedy_modularity_communities() (in module networkx.algorithms.community.modularity_max)": [[384, "networkx.algorithms.community.modularity_max.naive_greedy_modularity_communities"]], "modularity() (in module networkx.algorithms.community.quality)": [[385, "networkx.algorithms.community.quality.modularity"]], "partition_quality() (in module networkx.algorithms.community.quality)": [[386, "networkx.algorithms.community.quality.partition_quality"]], "articulation_points() (in module networkx.algorithms.components)": [[387, "networkx.algorithms.components.articulation_points"]], "attracting_components() (in module networkx.algorithms.components)": [[388, "networkx.algorithms.components.attracting_components"]], "biconnected_component_edges() (in module networkx.algorithms.components)": [[389, "networkx.algorithms.components.biconnected_component_edges"]], "biconnected_components() (in module networkx.algorithms.components)": [[390, "networkx.algorithms.components.biconnected_components"]], "condensation() (in module networkx.algorithms.components)": [[391, "networkx.algorithms.components.condensation"]], "connected_components() (in module networkx.algorithms.components)": [[392, "networkx.algorithms.components.connected_components"]], "is_attracting_component() (in module networkx.algorithms.components)": [[393, "networkx.algorithms.components.is_attracting_component"]], "is_biconnected() (in module networkx.algorithms.components)": [[394, "networkx.algorithms.components.is_biconnected"]], "is_connected() (in module networkx.algorithms.components)": [[395, "networkx.algorithms.components.is_connected"]], "is_semiconnected() (in module networkx.algorithms.components)": [[396, "networkx.algorithms.components.is_semiconnected"]], "is_strongly_connected() (in module networkx.algorithms.components)": [[397, "networkx.algorithms.components.is_strongly_connected"]], "is_weakly_connected() (in module networkx.algorithms.components)": [[398, "networkx.algorithms.components.is_weakly_connected"]], "kosaraju_strongly_connected_components() (in module networkx.algorithms.components)": [[399, "networkx.algorithms.components.kosaraju_strongly_connected_components"]], "node_connected_component() (in module networkx.algorithms.components)": [[400, "networkx.algorithms.components.node_connected_component"]], "number_attracting_components() (in module networkx.algorithms.components)": [[401, "networkx.algorithms.components.number_attracting_components"]], "number_connected_components() (in module networkx.algorithms.components)": [[402, "networkx.algorithms.components.number_connected_components"]], "number_strongly_connected_components() (in module networkx.algorithms.components)": [[403, "networkx.algorithms.components.number_strongly_connected_components"]], "number_weakly_connected_components() (in module networkx.algorithms.components)": [[404, "networkx.algorithms.components.number_weakly_connected_components"]], "strongly_connected_components() (in module networkx.algorithms.components)": [[405, "networkx.algorithms.components.strongly_connected_components"]], "strongly_connected_components_recursive() (in module networkx.algorithms.components)": [[406, "networkx.algorithms.components.strongly_connected_components_recursive"]], "weakly_connected_components() (in module networkx.algorithms.components)": [[407, "networkx.algorithms.components.weakly_connected_components"]], "all_pairs_node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[408, "networkx.algorithms.connectivity.connectivity.all_pairs_node_connectivity"]], "average_node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[409, "networkx.algorithms.connectivity.connectivity.average_node_connectivity"]], "edge_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[410, "networkx.algorithms.connectivity.connectivity.edge_connectivity"]], "local_edge_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[411, "networkx.algorithms.connectivity.connectivity.local_edge_connectivity"]], "local_node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[412, "networkx.algorithms.connectivity.connectivity.local_node_connectivity"]], "node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[413, "networkx.algorithms.connectivity.connectivity.node_connectivity"]], "minimum_edge_cut() (in module networkx.algorithms.connectivity.cuts)": [[414, "networkx.algorithms.connectivity.cuts.minimum_edge_cut"]], "minimum_node_cut() (in module networkx.algorithms.connectivity.cuts)": [[415, "networkx.algorithms.connectivity.cuts.minimum_node_cut"]], "minimum_st_edge_cut() (in module networkx.algorithms.connectivity.cuts)": [[416, "networkx.algorithms.connectivity.cuts.minimum_st_edge_cut"]], "minimum_st_node_cut() (in module networkx.algorithms.connectivity.cuts)": [[417, "networkx.algorithms.connectivity.cuts.minimum_st_node_cut"]], "edge_disjoint_paths() (in module networkx.algorithms.connectivity.disjoint_paths)": [[418, "networkx.algorithms.connectivity.disjoint_paths.edge_disjoint_paths"]], "node_disjoint_paths() (in module networkx.algorithms.connectivity.disjoint_paths)": [[419, "networkx.algorithms.connectivity.disjoint_paths.node_disjoint_paths"]], "is_k_edge_connected() (in module networkx.algorithms.connectivity.edge_augmentation)": [[420, "networkx.algorithms.connectivity.edge_augmentation.is_k_edge_connected"]], "is_locally_k_edge_connected() (in module networkx.algorithms.connectivity.edge_augmentation)": [[421, "networkx.algorithms.connectivity.edge_augmentation.is_locally_k_edge_connected"]], "k_edge_augmentation() (in module networkx.algorithms.connectivity.edge_augmentation)": [[422, "networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation"]], "edgecomponentauxgraph (class in networkx.algorithms.connectivity.edge_kcomponents)": [[423, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph"]], "__init__() (edgecomponentauxgraph method)": [[423, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.__init__"]], "bridge_components() (in module networkx.algorithms.connectivity.edge_kcomponents)": [[424, "networkx.algorithms.connectivity.edge_kcomponents.bridge_components"]], "k_edge_components() (in module networkx.algorithms.connectivity.edge_kcomponents)": [[425, "networkx.algorithms.connectivity.edge_kcomponents.k_edge_components"]], "k_edge_subgraphs() (in module networkx.algorithms.connectivity.edge_kcomponents)": [[426, "networkx.algorithms.connectivity.edge_kcomponents.k_edge_subgraphs"]], "k_components() (in module networkx.algorithms.connectivity.kcomponents)": [[427, "networkx.algorithms.connectivity.kcomponents.k_components"]], "all_node_cuts() (in module networkx.algorithms.connectivity.kcutsets)": [[428, "networkx.algorithms.connectivity.kcutsets.all_node_cuts"]], "stoer_wagner() (in module networkx.algorithms.connectivity.stoerwagner)": [[429, "networkx.algorithms.connectivity.stoerwagner.stoer_wagner"]], "build_auxiliary_edge_connectivity() (in module networkx.algorithms.connectivity.utils)": [[430, "networkx.algorithms.connectivity.utils.build_auxiliary_edge_connectivity"]], "build_auxiliary_node_connectivity() (in module networkx.algorithms.connectivity.utils)": [[431, "networkx.algorithms.connectivity.utils.build_auxiliary_node_connectivity"]], "core_number() (in module networkx.algorithms.core)": [[432, "networkx.algorithms.core.core_number"]], "k_core() (in module networkx.algorithms.core)": [[433, "networkx.algorithms.core.k_core"]], "k_corona() (in module networkx.algorithms.core)": [[434, "networkx.algorithms.core.k_corona"]], "k_crust() (in module networkx.algorithms.core)": [[435, "networkx.algorithms.core.k_crust"]], "k_shell() (in module networkx.algorithms.core)": [[436, "networkx.algorithms.core.k_shell"]], "k_truss() (in module networkx.algorithms.core)": [[437, "networkx.algorithms.core.k_truss"]], "onion_layers() (in module networkx.algorithms.core)": [[438, "networkx.algorithms.core.onion_layers"]], "is_edge_cover() (in module networkx.algorithms.covering)": [[439, "networkx.algorithms.covering.is_edge_cover"]], "min_edge_cover() (in module networkx.algorithms.covering)": [[440, "networkx.algorithms.covering.min_edge_cover"]], "boundary_expansion() (in module networkx.algorithms.cuts)": [[441, "networkx.algorithms.cuts.boundary_expansion"]], "conductance() (in module networkx.algorithms.cuts)": [[442, "networkx.algorithms.cuts.conductance"]], "cut_size() (in module networkx.algorithms.cuts)": [[443, "networkx.algorithms.cuts.cut_size"]], "edge_expansion() (in module networkx.algorithms.cuts)": [[444, "networkx.algorithms.cuts.edge_expansion"]], "mixing_expansion() (in module networkx.algorithms.cuts)": [[445, "networkx.algorithms.cuts.mixing_expansion"]], "node_expansion() (in module networkx.algorithms.cuts)": [[446, "networkx.algorithms.cuts.node_expansion"]], "normalized_cut_size() (in module networkx.algorithms.cuts)": [[447, "networkx.algorithms.cuts.normalized_cut_size"]], "volume() (in module networkx.algorithms.cuts)": [[448, "networkx.algorithms.cuts.volume"]], "cycle_basis() (in module networkx.algorithms.cycles)": [[449, "networkx.algorithms.cycles.cycle_basis"]], "find_cycle() (in module networkx.algorithms.cycles)": [[450, "networkx.algorithms.cycles.find_cycle"]], "minimum_cycle_basis() (in module networkx.algorithms.cycles)": [[451, "networkx.algorithms.cycles.minimum_cycle_basis"]], "recursive_simple_cycles() (in module networkx.algorithms.cycles)": [[452, "networkx.algorithms.cycles.recursive_simple_cycles"]], "simple_cycles() (in module networkx.algorithms.cycles)": [[453, "networkx.algorithms.cycles.simple_cycles"]], "d_separated() (in module networkx.algorithms.d_separation)": [[454, "networkx.algorithms.d_separation.d_separated"]], "all_topological_sorts() (in module networkx.algorithms.dag)": [[455, "networkx.algorithms.dag.all_topological_sorts"]], "ancestors() (in module networkx.algorithms.dag)": [[456, "networkx.algorithms.dag.ancestors"]], "antichains() (in module networkx.algorithms.dag)": [[457, "networkx.algorithms.dag.antichains"]], "dag_longest_path() (in module networkx.algorithms.dag)": [[458, "networkx.algorithms.dag.dag_longest_path"]], "dag_longest_path_length() (in module networkx.algorithms.dag)": [[459, "networkx.algorithms.dag.dag_longest_path_length"]], "dag_to_branching() (in module networkx.algorithms.dag)": [[460, "networkx.algorithms.dag.dag_to_branching"]], "descendants() (in module networkx.algorithms.dag)": [[461, "networkx.algorithms.dag.descendants"]], "is_aperiodic() (in module networkx.algorithms.dag)": [[462, "networkx.algorithms.dag.is_aperiodic"]], "is_directed_acyclic_graph() (in module networkx.algorithms.dag)": [[463, "networkx.algorithms.dag.is_directed_acyclic_graph"]], "lexicographical_topological_sort() (in module networkx.algorithms.dag)": [[464, "networkx.algorithms.dag.lexicographical_topological_sort"]], "topological_generations() (in module networkx.algorithms.dag)": [[465, "networkx.algorithms.dag.topological_generations"]], "topological_sort() (in module networkx.algorithms.dag)": [[466, "networkx.algorithms.dag.topological_sort"]], "transitive_closure() (in module networkx.algorithms.dag)": [[467, "networkx.algorithms.dag.transitive_closure"]], "transitive_closure_dag() (in module networkx.algorithms.dag)": [[468, "networkx.algorithms.dag.transitive_closure_dag"]], "transitive_reduction() (in module networkx.algorithms.dag)": [[469, "networkx.algorithms.dag.transitive_reduction"]], "barycenter() (in module networkx.algorithms.distance_measures)": [[470, "networkx.algorithms.distance_measures.barycenter"]], "center() (in module networkx.algorithms.distance_measures)": [[471, "networkx.algorithms.distance_measures.center"]], "diameter() (in module networkx.algorithms.distance_measures)": [[472, "networkx.algorithms.distance_measures.diameter"]], "eccentricity() (in module networkx.algorithms.distance_measures)": [[473, "networkx.algorithms.distance_measures.eccentricity"]], "periphery() (in module networkx.algorithms.distance_measures)": [[474, "networkx.algorithms.distance_measures.periphery"]], "radius() (in module networkx.algorithms.distance_measures)": [[475, "networkx.algorithms.distance_measures.radius"]], "resistance_distance() (in module networkx.algorithms.distance_measures)": [[476, "networkx.algorithms.distance_measures.resistance_distance"]], "global_parameters() (in module networkx.algorithms.distance_regular)": [[477, "networkx.algorithms.distance_regular.global_parameters"]], "intersection_array() (in module networkx.algorithms.distance_regular)": [[478, "networkx.algorithms.distance_regular.intersection_array"]], "is_distance_regular() (in module networkx.algorithms.distance_regular)": [[479, "networkx.algorithms.distance_regular.is_distance_regular"]], "is_strongly_regular() (in module networkx.algorithms.distance_regular)": [[480, "networkx.algorithms.distance_regular.is_strongly_regular"]], "dominance_frontiers() (in module networkx.algorithms.dominance)": [[481, "networkx.algorithms.dominance.dominance_frontiers"]], "immediate_dominators() (in module networkx.algorithms.dominance)": [[482, "networkx.algorithms.dominance.immediate_dominators"]], "dominating_set() (in module networkx.algorithms.dominating)": [[483, "networkx.algorithms.dominating.dominating_set"]], "is_dominating_set() (in module networkx.algorithms.dominating)": [[484, "networkx.algorithms.dominating.is_dominating_set"]], "efficiency() (in module networkx.algorithms.efficiency_measures)": [[485, "networkx.algorithms.efficiency_measures.efficiency"]], "global_efficiency() (in module networkx.algorithms.efficiency_measures)": [[486, "networkx.algorithms.efficiency_measures.global_efficiency"]], "local_efficiency() (in module networkx.algorithms.efficiency_measures)": [[487, "networkx.algorithms.efficiency_measures.local_efficiency"]], "eulerian_circuit() (in module networkx.algorithms.euler)": [[488, "networkx.algorithms.euler.eulerian_circuit"]], "eulerian_path() (in module networkx.algorithms.euler)": [[489, "networkx.algorithms.euler.eulerian_path"]], "eulerize() (in module networkx.algorithms.euler)": [[490, "networkx.algorithms.euler.eulerize"]], "has_eulerian_path() (in module networkx.algorithms.euler)": [[491, "networkx.algorithms.euler.has_eulerian_path"]], "is_eulerian() (in module networkx.algorithms.euler)": [[492, "networkx.algorithms.euler.is_eulerian"]], "is_semieulerian() (in module networkx.algorithms.euler)": [[493, "networkx.algorithms.euler.is_semieulerian"]], "boykov_kolmogorov() (in module networkx.algorithms.flow)": [[494, "networkx.algorithms.flow.boykov_kolmogorov"]], "build_residual_network() (in module networkx.algorithms.flow)": [[495, "networkx.algorithms.flow.build_residual_network"]], "capacity_scaling() (in module networkx.algorithms.flow)": [[496, "networkx.algorithms.flow.capacity_scaling"]], "cost_of_flow() (in module networkx.algorithms.flow)": [[497, "networkx.algorithms.flow.cost_of_flow"]], "dinitz() (in module networkx.algorithms.flow)": [[498, "networkx.algorithms.flow.dinitz"]], "edmonds_karp() (in module networkx.algorithms.flow)": [[499, "networkx.algorithms.flow.edmonds_karp"]], "gomory_hu_tree() (in module networkx.algorithms.flow)": [[500, "networkx.algorithms.flow.gomory_hu_tree"]], "max_flow_min_cost() (in module networkx.algorithms.flow)": [[501, "networkx.algorithms.flow.max_flow_min_cost"]], "maximum_flow() (in module networkx.algorithms.flow)": [[502, "networkx.algorithms.flow.maximum_flow"]], "maximum_flow_value() (in module networkx.algorithms.flow)": [[503, "networkx.algorithms.flow.maximum_flow_value"]], "min_cost_flow() (in module networkx.algorithms.flow)": [[504, "networkx.algorithms.flow.min_cost_flow"]], "min_cost_flow_cost() (in module networkx.algorithms.flow)": [[505, "networkx.algorithms.flow.min_cost_flow_cost"]], "minimum_cut() (in module networkx.algorithms.flow)": [[506, "networkx.algorithms.flow.minimum_cut"]], "minimum_cut_value() (in module networkx.algorithms.flow)": [[507, "networkx.algorithms.flow.minimum_cut_value"]], "network_simplex() (in module networkx.algorithms.flow)": [[508, "networkx.algorithms.flow.network_simplex"]], "preflow_push() (in module networkx.algorithms.flow)": [[509, "networkx.algorithms.flow.preflow_push"]], "shortest_augmenting_path() (in module networkx.algorithms.flow)": [[510, "networkx.algorithms.flow.shortest_augmenting_path"]], "weisfeiler_lehman_graph_hash() (in module networkx.algorithms.graph_hashing)": [[511, "networkx.algorithms.graph_hashing.weisfeiler_lehman_graph_hash"]], "weisfeiler_lehman_subgraph_hashes() (in module networkx.algorithms.graph_hashing)": [[512, "networkx.algorithms.graph_hashing.weisfeiler_lehman_subgraph_hashes"]], "is_digraphical() (in module networkx.algorithms.graphical)": [[513, "networkx.algorithms.graphical.is_digraphical"]], "is_graphical() (in module networkx.algorithms.graphical)": [[514, "networkx.algorithms.graphical.is_graphical"]], "is_multigraphical() (in module networkx.algorithms.graphical)": [[515, "networkx.algorithms.graphical.is_multigraphical"]], "is_pseudographical() (in module networkx.algorithms.graphical)": [[516, "networkx.algorithms.graphical.is_pseudographical"]], "is_valid_degree_sequence_erdos_gallai() (in module networkx.algorithms.graphical)": [[517, "networkx.algorithms.graphical.is_valid_degree_sequence_erdos_gallai"]], "is_valid_degree_sequence_havel_hakimi() (in module networkx.algorithms.graphical)": [[518, "networkx.algorithms.graphical.is_valid_degree_sequence_havel_hakimi"]], "flow_hierarchy() (in module networkx.algorithms.hierarchy)": [[519, "networkx.algorithms.hierarchy.flow_hierarchy"]], "is_kl_connected() (in module networkx.algorithms.hybrid)": [[520, "networkx.algorithms.hybrid.is_kl_connected"]], "kl_connected_subgraph() (in module networkx.algorithms.hybrid)": [[521, "networkx.algorithms.hybrid.kl_connected_subgraph"]], "is_isolate() (in module networkx.algorithms.isolate)": [[522, "networkx.algorithms.isolate.is_isolate"]], "isolates() (in module networkx.algorithms.isolate)": [[523, "networkx.algorithms.isolate.isolates"]], "number_of_isolates() (in module networkx.algorithms.isolate)": [[524, "networkx.algorithms.isolate.number_of_isolates"]], "__init__() (digraphmatcher method)": [[525, "networkx.algorithms.isomorphism.DiGraphMatcher.__init__"]], "candidate_pairs_iter() (digraphmatcher method)": [[526, "networkx.algorithms.isomorphism.DiGraphMatcher.candidate_pairs_iter"]], "initialize() (digraphmatcher method)": [[527, "networkx.algorithms.isomorphism.DiGraphMatcher.initialize"]], "is_isomorphic() (digraphmatcher method)": [[528, "networkx.algorithms.isomorphism.DiGraphMatcher.is_isomorphic"]], "isomorphisms_iter() (digraphmatcher method)": [[529, "networkx.algorithms.isomorphism.DiGraphMatcher.isomorphisms_iter"]], "match() (digraphmatcher method)": [[530, "networkx.algorithms.isomorphism.DiGraphMatcher.match"]], "semantic_feasibility() (digraphmatcher method)": [[531, "networkx.algorithms.isomorphism.DiGraphMatcher.semantic_feasibility"]], "subgraph_is_isomorphic() (digraphmatcher method)": [[532, "networkx.algorithms.isomorphism.DiGraphMatcher.subgraph_is_isomorphic"]], "subgraph_isomorphisms_iter() (digraphmatcher method)": [[533, "networkx.algorithms.isomorphism.DiGraphMatcher.subgraph_isomorphisms_iter"]], "syntactic_feasibility() (digraphmatcher method)": [[534, "networkx.algorithms.isomorphism.DiGraphMatcher.syntactic_feasibility"]], "__init__() (graphmatcher method)": [[535, "networkx.algorithms.isomorphism.GraphMatcher.__init__"]], "candidate_pairs_iter() (graphmatcher method)": [[536, "networkx.algorithms.isomorphism.GraphMatcher.candidate_pairs_iter"]], "initialize() (graphmatcher method)": [[537, "networkx.algorithms.isomorphism.GraphMatcher.initialize"]], "is_isomorphic() (graphmatcher method)": [[538, "networkx.algorithms.isomorphism.GraphMatcher.is_isomorphic"]], "isomorphisms_iter() (graphmatcher method)": [[539, "networkx.algorithms.isomorphism.GraphMatcher.isomorphisms_iter"]], "match() (graphmatcher method)": [[540, "networkx.algorithms.isomorphism.GraphMatcher.match"]], "semantic_feasibility() (graphmatcher method)": [[541, "networkx.algorithms.isomorphism.GraphMatcher.semantic_feasibility"]], "subgraph_is_isomorphic() (graphmatcher method)": [[542, "networkx.algorithms.isomorphism.GraphMatcher.subgraph_is_isomorphic"]], "subgraph_isomorphisms_iter() (graphmatcher method)": [[543, "networkx.algorithms.isomorphism.GraphMatcher.subgraph_isomorphisms_iter"]], "syntactic_feasibility() (graphmatcher method)": [[544, "networkx.algorithms.isomorphism.GraphMatcher.syntactic_feasibility"]], "ismags (class in networkx.algorithms.isomorphism)": [[545, "networkx.algorithms.isomorphism.ISMAGS"]], "__init__() (ismags method)": [[545, "networkx.algorithms.isomorphism.ISMAGS.__init__"]], "categorical_edge_match() (in module networkx.algorithms.isomorphism)": [[546, "networkx.algorithms.isomorphism.categorical_edge_match"]], "categorical_multiedge_match() (in module networkx.algorithms.isomorphism)": [[547, "networkx.algorithms.isomorphism.categorical_multiedge_match"]], "categorical_node_match() (in module networkx.algorithms.isomorphism)": [[548, "networkx.algorithms.isomorphism.categorical_node_match"]], "could_be_isomorphic() (in module networkx.algorithms.isomorphism)": [[549, "networkx.algorithms.isomorphism.could_be_isomorphic"]], "fast_could_be_isomorphic() (in module networkx.algorithms.isomorphism)": [[550, "networkx.algorithms.isomorphism.fast_could_be_isomorphic"]], "faster_could_be_isomorphic() (in module networkx.algorithms.isomorphism)": [[551, "networkx.algorithms.isomorphism.faster_could_be_isomorphic"]], "generic_edge_match() (in module networkx.algorithms.isomorphism)": [[552, "networkx.algorithms.isomorphism.generic_edge_match"]], "generic_multiedge_match() (in module networkx.algorithms.isomorphism)": [[553, "networkx.algorithms.isomorphism.generic_multiedge_match"]], "generic_node_match() (in module networkx.algorithms.isomorphism)": [[554, "networkx.algorithms.isomorphism.generic_node_match"]], "is_isomorphic() (in module networkx.algorithms.isomorphism)": [[555, "networkx.algorithms.isomorphism.is_isomorphic"]], "numerical_edge_match() (in module networkx.algorithms.isomorphism)": [[556, "networkx.algorithms.isomorphism.numerical_edge_match"]], "numerical_multiedge_match() (in module networkx.algorithms.isomorphism)": [[557, "networkx.algorithms.isomorphism.numerical_multiedge_match"]], "numerical_node_match() (in module networkx.algorithms.isomorphism)": [[558, "networkx.algorithms.isomorphism.numerical_node_match"]], "rooted_tree_isomorphism() (in module networkx.algorithms.isomorphism.tree_isomorphism)": [[559, "networkx.algorithms.isomorphism.tree_isomorphism.rooted_tree_isomorphism"]], "tree_isomorphism() (in module networkx.algorithms.isomorphism.tree_isomorphism)": [[560, "networkx.algorithms.isomorphism.tree_isomorphism.tree_isomorphism"]], "vf2pp_all_isomorphisms() (in module networkx.algorithms.isomorphism.vf2pp)": [[561, "networkx.algorithms.isomorphism.vf2pp.vf2pp_all_isomorphisms"]], "vf2pp_is_isomorphic() (in module networkx.algorithms.isomorphism.vf2pp)": [[562, "networkx.algorithms.isomorphism.vf2pp.vf2pp_is_isomorphic"]], "vf2pp_isomorphism() (in module networkx.algorithms.isomorphism.vf2pp)": [[563, "networkx.algorithms.isomorphism.vf2pp.vf2pp_isomorphism"]], "hits() (in module networkx.algorithms.link_analysis.hits_alg)": [[564, "networkx.algorithms.link_analysis.hits_alg.hits"]], "google_matrix() (in module networkx.algorithms.link_analysis.pagerank_alg)": [[565, "networkx.algorithms.link_analysis.pagerank_alg.google_matrix"]], "pagerank() (in module networkx.algorithms.link_analysis.pagerank_alg)": [[566, "networkx.algorithms.link_analysis.pagerank_alg.pagerank"]], "adamic_adar_index() (in module networkx.algorithms.link_prediction)": [[567, "networkx.algorithms.link_prediction.adamic_adar_index"]], "cn_soundarajan_hopcroft() (in module networkx.algorithms.link_prediction)": [[568, "networkx.algorithms.link_prediction.cn_soundarajan_hopcroft"]], "common_neighbor_centrality() (in module networkx.algorithms.link_prediction)": [[569, "networkx.algorithms.link_prediction.common_neighbor_centrality"]], "jaccard_coefficient() (in module networkx.algorithms.link_prediction)": [[570, "networkx.algorithms.link_prediction.jaccard_coefficient"]], "preferential_attachment() (in module networkx.algorithms.link_prediction)": [[571, "networkx.algorithms.link_prediction.preferential_attachment"]], "ra_index_soundarajan_hopcroft() (in module networkx.algorithms.link_prediction)": [[572, "networkx.algorithms.link_prediction.ra_index_soundarajan_hopcroft"]], "resource_allocation_index() (in module networkx.algorithms.link_prediction)": [[573, "networkx.algorithms.link_prediction.resource_allocation_index"]], "within_inter_cluster() (in module networkx.algorithms.link_prediction)": [[574, "networkx.algorithms.link_prediction.within_inter_cluster"]], "all_pairs_lowest_common_ancestor() (in module networkx.algorithms.lowest_common_ancestors)": [[575, "networkx.algorithms.lowest_common_ancestors.all_pairs_lowest_common_ancestor"]], "lowest_common_ancestor() (in module networkx.algorithms.lowest_common_ancestors)": [[576, "networkx.algorithms.lowest_common_ancestors.lowest_common_ancestor"]], "tree_all_pairs_lowest_common_ancestor() (in module networkx.algorithms.lowest_common_ancestors)": [[577, "networkx.algorithms.lowest_common_ancestors.tree_all_pairs_lowest_common_ancestor"]], "is_matching() (in module networkx.algorithms.matching)": [[578, "networkx.algorithms.matching.is_matching"]], "is_maximal_matching() (in module networkx.algorithms.matching)": [[579, "networkx.algorithms.matching.is_maximal_matching"]], "is_perfect_matching() (in module networkx.algorithms.matching)": [[580, "networkx.algorithms.matching.is_perfect_matching"]], "max_weight_matching() (in module networkx.algorithms.matching)": [[581, "networkx.algorithms.matching.max_weight_matching"]], "maximal_matching() (in module networkx.algorithms.matching)": [[582, "networkx.algorithms.matching.maximal_matching"]], "min_weight_matching() (in module networkx.algorithms.matching)": [[583, "networkx.algorithms.matching.min_weight_matching"]], "contracted_edge() (in module networkx.algorithms.minors)": [[584, "networkx.algorithms.minors.contracted_edge"]], "contracted_nodes() (in module networkx.algorithms.minors)": [[585, "networkx.algorithms.minors.contracted_nodes"]], "equivalence_classes() (in module networkx.algorithms.minors)": [[586, "networkx.algorithms.minors.equivalence_classes"]], "identified_nodes() (in module networkx.algorithms.minors)": [[587, "networkx.algorithms.minors.identified_nodes"]], "quotient_graph() (in module networkx.algorithms.minors)": [[588, "networkx.algorithms.minors.quotient_graph"]], "maximal_independent_set() (in module networkx.algorithms.mis)": [[589, "networkx.algorithms.mis.maximal_independent_set"]], "moral_graph() (in module networkx.algorithms.moral)": [[590, "networkx.algorithms.moral.moral_graph"]], "harmonic_function() (in module networkx.algorithms.node_classification)": [[591, "networkx.algorithms.node_classification.harmonic_function"]], "local_and_global_consistency() (in module networkx.algorithms.node_classification)": [[592, "networkx.algorithms.node_classification.local_and_global_consistency"]], "non_randomness() (in module networkx.algorithms.non_randomness)": [[593, "networkx.algorithms.non_randomness.non_randomness"]], "compose_all() (in module networkx.algorithms.operators.all)": [[594, "networkx.algorithms.operators.all.compose_all"]], "disjoint_union_all() (in module networkx.algorithms.operators.all)": [[595, "networkx.algorithms.operators.all.disjoint_union_all"]], "intersection_all() (in module networkx.algorithms.operators.all)": [[596, "networkx.algorithms.operators.all.intersection_all"]], "union_all() (in module networkx.algorithms.operators.all)": [[597, "networkx.algorithms.operators.all.union_all"]], "compose() (in module networkx.algorithms.operators.binary)": [[598, "networkx.algorithms.operators.binary.compose"]], "difference() (in module networkx.algorithms.operators.binary)": [[599, "networkx.algorithms.operators.binary.difference"]], "disjoint_union() (in module networkx.algorithms.operators.binary)": [[600, "networkx.algorithms.operators.binary.disjoint_union"]], "full_join() (in module networkx.algorithms.operators.binary)": [[601, "networkx.algorithms.operators.binary.full_join"]], "intersection() (in module networkx.algorithms.operators.binary)": [[602, "networkx.algorithms.operators.binary.intersection"]], "symmetric_difference() (in module networkx.algorithms.operators.binary)": [[603, "networkx.algorithms.operators.binary.symmetric_difference"]], "union() (in module networkx.algorithms.operators.binary)": [[604, "networkx.algorithms.operators.binary.union"]], "cartesian_product() (in module networkx.algorithms.operators.product)": [[605, "networkx.algorithms.operators.product.cartesian_product"]], "corona_product() (in module networkx.algorithms.operators.product)": [[606, "networkx.algorithms.operators.product.corona_product"]], "lexicographic_product() (in module networkx.algorithms.operators.product)": [[607, "networkx.algorithms.operators.product.lexicographic_product"]], "power() (in module networkx.algorithms.operators.product)": [[608, "networkx.algorithms.operators.product.power"]], "rooted_product() (in module networkx.algorithms.operators.product)": [[609, "networkx.algorithms.operators.product.rooted_product"]], "strong_product() (in module networkx.algorithms.operators.product)": [[610, "networkx.algorithms.operators.product.strong_product"]], "tensor_product() (in module networkx.algorithms.operators.product)": [[611, "networkx.algorithms.operators.product.tensor_product"]], "complement() (in module networkx.algorithms.operators.unary)": [[612, "networkx.algorithms.operators.unary.complement"]], "reverse() (in module networkx.algorithms.operators.unary)": [[613, "networkx.algorithms.operators.unary.reverse"]], "combinatorial_embedding_to_pos() (in module networkx.algorithms.planar_drawing)": [[614, "networkx.algorithms.planar_drawing.combinatorial_embedding_to_pos"]], "planarembedding (class in networkx.algorithms.planarity)": [[615, "networkx.algorithms.planarity.PlanarEmbedding"]], "__init__() (planarembedding method)": [[615, "networkx.algorithms.planarity.PlanarEmbedding.__init__"]], "check_planarity() (in module networkx.algorithms.planarity)": [[616, "networkx.algorithms.planarity.check_planarity"]], "is_planar() (in module networkx.algorithms.planarity)": [[617, "networkx.algorithms.planarity.is_planar"]], "chromatic_polynomial() (in module networkx.algorithms.polynomials)": [[618, "networkx.algorithms.polynomials.chromatic_polynomial"]], "tutte_polynomial() (in module networkx.algorithms.polynomials)": [[619, "networkx.algorithms.polynomials.tutte_polynomial"]], "overall_reciprocity() (in module networkx.algorithms.reciprocity)": [[620, "networkx.algorithms.reciprocity.overall_reciprocity"]], "reciprocity() (in module networkx.algorithms.reciprocity)": [[621, "networkx.algorithms.reciprocity.reciprocity"]], "is_k_regular() (in module networkx.algorithms.regular)": [[622, "networkx.algorithms.regular.is_k_regular"]], "is_regular() (in module networkx.algorithms.regular)": [[623, "networkx.algorithms.regular.is_regular"]], "k_factor() (in module networkx.algorithms.regular)": [[624, "networkx.algorithms.regular.k_factor"]], "rich_club_coefficient() (in module networkx.algorithms.richclub)": [[625, "networkx.algorithms.richclub.rich_club_coefficient"]], "astar_path() (in module networkx.algorithms.shortest_paths.astar)": [[626, "networkx.algorithms.shortest_paths.astar.astar_path"]], "astar_path_length() (in module networkx.algorithms.shortest_paths.astar)": [[627, "networkx.algorithms.shortest_paths.astar.astar_path_length"]], "floyd_warshall() (in module networkx.algorithms.shortest_paths.dense)": [[628, "networkx.algorithms.shortest_paths.dense.floyd_warshall"]], "floyd_warshall_numpy() (in module networkx.algorithms.shortest_paths.dense)": [[629, "networkx.algorithms.shortest_paths.dense.floyd_warshall_numpy"]], "floyd_warshall_predecessor_and_distance() (in module networkx.algorithms.shortest_paths.dense)": [[630, "networkx.algorithms.shortest_paths.dense.floyd_warshall_predecessor_and_distance"]], "reconstruct_path() (in module networkx.algorithms.shortest_paths.dense)": [[631, "networkx.algorithms.shortest_paths.dense.reconstruct_path"]], "all_shortest_paths() (in module networkx.algorithms.shortest_paths.generic)": [[632, "networkx.algorithms.shortest_paths.generic.all_shortest_paths"]], "average_shortest_path_length() (in module networkx.algorithms.shortest_paths.generic)": [[633, "networkx.algorithms.shortest_paths.generic.average_shortest_path_length"]], "has_path() (in module networkx.algorithms.shortest_paths.generic)": [[634, "networkx.algorithms.shortest_paths.generic.has_path"]], "shortest_path() (in module networkx.algorithms.shortest_paths.generic)": [[635, "networkx.algorithms.shortest_paths.generic.shortest_path"]], "shortest_path_length() (in module networkx.algorithms.shortest_paths.generic)": [[636, "networkx.algorithms.shortest_paths.generic.shortest_path_length"]], "all_pairs_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[637, "networkx.algorithms.shortest_paths.unweighted.all_pairs_shortest_path"]], "all_pairs_shortest_path_length() (in module networkx.algorithms.shortest_paths.unweighted)": [[638, "networkx.algorithms.shortest_paths.unweighted.all_pairs_shortest_path_length"]], "bidirectional_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[639, "networkx.algorithms.shortest_paths.unweighted.bidirectional_shortest_path"]], "predecessor() (in module networkx.algorithms.shortest_paths.unweighted)": [[640, "networkx.algorithms.shortest_paths.unweighted.predecessor"]], "single_source_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[641, "networkx.algorithms.shortest_paths.unweighted.single_source_shortest_path"]], "single_source_shortest_path_length() (in module networkx.algorithms.shortest_paths.unweighted)": [[642, "networkx.algorithms.shortest_paths.unweighted.single_source_shortest_path_length"]], "single_target_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[643, "networkx.algorithms.shortest_paths.unweighted.single_target_shortest_path"]], "single_target_shortest_path_length() (in module networkx.algorithms.shortest_paths.unweighted)": [[644, "networkx.algorithms.shortest_paths.unweighted.single_target_shortest_path_length"]], "all_pairs_bellman_ford_path() (in module networkx.algorithms.shortest_paths.weighted)": [[645, "networkx.algorithms.shortest_paths.weighted.all_pairs_bellman_ford_path"]], "all_pairs_bellman_ford_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[646, "networkx.algorithms.shortest_paths.weighted.all_pairs_bellman_ford_path_length"]], "all_pairs_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[647, "networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra"]], "all_pairs_dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[648, "networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra_path"]], "all_pairs_dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[649, "networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra_path_length"]], "bellman_ford_path() (in module networkx.algorithms.shortest_paths.weighted)": [[650, "networkx.algorithms.shortest_paths.weighted.bellman_ford_path"]], "bellman_ford_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[651, "networkx.algorithms.shortest_paths.weighted.bellman_ford_path_length"]], "bellman_ford_predecessor_and_distance() (in module networkx.algorithms.shortest_paths.weighted)": [[652, "networkx.algorithms.shortest_paths.weighted.bellman_ford_predecessor_and_distance"]], "bidirectional_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[653, "networkx.algorithms.shortest_paths.weighted.bidirectional_dijkstra"]], "dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[654, "networkx.algorithms.shortest_paths.weighted.dijkstra_path"]], "dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[655, "networkx.algorithms.shortest_paths.weighted.dijkstra_path_length"]], "dijkstra_predecessor_and_distance() (in module networkx.algorithms.shortest_paths.weighted)": [[656, "networkx.algorithms.shortest_paths.weighted.dijkstra_predecessor_and_distance"]], "find_negative_cycle() (in module networkx.algorithms.shortest_paths.weighted)": [[657, "networkx.algorithms.shortest_paths.weighted.find_negative_cycle"]], "goldberg_radzik() (in module networkx.algorithms.shortest_paths.weighted)": [[658, "networkx.algorithms.shortest_paths.weighted.goldberg_radzik"]], "johnson() (in module networkx.algorithms.shortest_paths.weighted)": [[659, "networkx.algorithms.shortest_paths.weighted.johnson"]], "multi_source_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[660, "networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra"]], "multi_source_dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[661, "networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra_path"]], "multi_source_dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[662, "networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra_path_length"]], "negative_edge_cycle() (in module networkx.algorithms.shortest_paths.weighted)": [[663, "networkx.algorithms.shortest_paths.weighted.negative_edge_cycle"]], "single_source_bellman_ford() (in module networkx.algorithms.shortest_paths.weighted)": [[664, "networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford"]], "single_source_bellman_ford_path() (in module networkx.algorithms.shortest_paths.weighted)": [[665, "networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford_path"]], "single_source_bellman_ford_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[666, "networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford_path_length"]], "single_source_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[667, "networkx.algorithms.shortest_paths.weighted.single_source_dijkstra"]], "single_source_dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[668, "networkx.algorithms.shortest_paths.weighted.single_source_dijkstra_path"]], "single_source_dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[669, "networkx.algorithms.shortest_paths.weighted.single_source_dijkstra_path_length"]], "generate_random_paths() (in module networkx.algorithms.similarity)": [[670, "networkx.algorithms.similarity.generate_random_paths"]], "graph_edit_distance() (in module networkx.algorithms.similarity)": [[671, "networkx.algorithms.similarity.graph_edit_distance"]], "optimal_edit_paths() (in module networkx.algorithms.similarity)": [[672, "networkx.algorithms.similarity.optimal_edit_paths"]], "optimize_edit_paths() (in module networkx.algorithms.similarity)": [[673, "networkx.algorithms.similarity.optimize_edit_paths"]], "optimize_graph_edit_distance() (in module networkx.algorithms.similarity)": [[674, "networkx.algorithms.similarity.optimize_graph_edit_distance"]], "panther_similarity() (in module networkx.algorithms.similarity)": [[675, "networkx.algorithms.similarity.panther_similarity"]], "simrank_similarity() (in module networkx.algorithms.similarity)": [[676, "networkx.algorithms.similarity.simrank_similarity"]], "all_simple_edge_paths() (in module networkx.algorithms.simple_paths)": [[677, "networkx.algorithms.simple_paths.all_simple_edge_paths"]], "all_simple_paths() (in module networkx.algorithms.simple_paths)": [[678, "networkx.algorithms.simple_paths.all_simple_paths"]], "is_simple_path() (in module networkx.algorithms.simple_paths)": [[679, "networkx.algorithms.simple_paths.is_simple_path"]], "shortest_simple_paths() (in module networkx.algorithms.simple_paths)": [[680, "networkx.algorithms.simple_paths.shortest_simple_paths"]], "lattice_reference() (in module networkx.algorithms.smallworld)": [[681, "networkx.algorithms.smallworld.lattice_reference"]], "omega() (in module networkx.algorithms.smallworld)": [[682, "networkx.algorithms.smallworld.omega"]], "random_reference() (in module networkx.algorithms.smallworld)": [[683, "networkx.algorithms.smallworld.random_reference"]], "sigma() (in module networkx.algorithms.smallworld)": [[684, "networkx.algorithms.smallworld.sigma"]], "s_metric() (in module networkx.algorithms.smetric)": [[685, "networkx.algorithms.smetric.s_metric"]], "spanner() (in module networkx.algorithms.sparsifiers)": [[686, "networkx.algorithms.sparsifiers.spanner"]], "constraint() (in module networkx.algorithms.structuralholes)": [[687, "networkx.algorithms.structuralholes.constraint"]], "effective_size() (in module networkx.algorithms.structuralholes)": [[688, "networkx.algorithms.structuralholes.effective_size"]], "local_constraint() (in module networkx.algorithms.structuralholes)": [[689, "networkx.algorithms.structuralholes.local_constraint"]], "dedensify() (in module networkx.algorithms.summarization)": [[690, "networkx.algorithms.summarization.dedensify"]], "snap_aggregation() (in module networkx.algorithms.summarization)": [[691, "networkx.algorithms.summarization.snap_aggregation"]], "connected_double_edge_swap() (in module networkx.algorithms.swap)": [[692, "networkx.algorithms.swap.connected_double_edge_swap"]], "directed_edge_swap() (in module networkx.algorithms.swap)": [[693, "networkx.algorithms.swap.directed_edge_swap"]], "double_edge_swap() (in module networkx.algorithms.swap)": [[694, "networkx.algorithms.swap.double_edge_swap"]], "find_threshold_graph() (in module networkx.algorithms.threshold)": [[695, "networkx.algorithms.threshold.find_threshold_graph"]], "is_threshold_graph() (in module networkx.algorithms.threshold)": [[696, "networkx.algorithms.threshold.is_threshold_graph"]], "hamiltonian_path() (in module networkx.algorithms.tournament)": [[697, "networkx.algorithms.tournament.hamiltonian_path"]], "is_reachable() (in module networkx.algorithms.tournament)": [[698, "networkx.algorithms.tournament.is_reachable"]], "is_strongly_connected() (in module networkx.algorithms.tournament)": [[699, "networkx.algorithms.tournament.is_strongly_connected"]], "is_tournament() (in module networkx.algorithms.tournament)": [[700, "networkx.algorithms.tournament.is_tournament"]], "random_tournament() (in module networkx.algorithms.tournament)": [[701, "networkx.algorithms.tournament.random_tournament"]], "score_sequence() (in module networkx.algorithms.tournament)": [[702, "networkx.algorithms.tournament.score_sequence"]], "bfs_beam_edges() (in module networkx.algorithms.traversal.beamsearch)": [[703, "networkx.algorithms.traversal.beamsearch.bfs_beam_edges"]], "bfs_edges() (in module networkx.algorithms.traversal.breadth_first_search)": [[704, "networkx.algorithms.traversal.breadth_first_search.bfs_edges"]], "bfs_layers() (in module networkx.algorithms.traversal.breadth_first_search)": [[705, "networkx.algorithms.traversal.breadth_first_search.bfs_layers"]], "bfs_predecessors() (in module networkx.algorithms.traversal.breadth_first_search)": [[706, "networkx.algorithms.traversal.breadth_first_search.bfs_predecessors"]], "bfs_successors() (in module networkx.algorithms.traversal.breadth_first_search)": [[707, "networkx.algorithms.traversal.breadth_first_search.bfs_successors"]], "bfs_tree() (in module networkx.algorithms.traversal.breadth_first_search)": [[708, "networkx.algorithms.traversal.breadth_first_search.bfs_tree"]], "descendants_at_distance() (in module networkx.algorithms.traversal.breadth_first_search)": [[709, "networkx.algorithms.traversal.breadth_first_search.descendants_at_distance"]], "dfs_edges() (in module networkx.algorithms.traversal.depth_first_search)": [[710, "networkx.algorithms.traversal.depth_first_search.dfs_edges"]], "dfs_labeled_edges() (in module networkx.algorithms.traversal.depth_first_search)": [[711, "networkx.algorithms.traversal.depth_first_search.dfs_labeled_edges"]], "dfs_postorder_nodes() (in module networkx.algorithms.traversal.depth_first_search)": [[712, "networkx.algorithms.traversal.depth_first_search.dfs_postorder_nodes"]], "dfs_predecessors() (in module networkx.algorithms.traversal.depth_first_search)": [[713, "networkx.algorithms.traversal.depth_first_search.dfs_predecessors"]], "dfs_preorder_nodes() (in module networkx.algorithms.traversal.depth_first_search)": [[714, "networkx.algorithms.traversal.depth_first_search.dfs_preorder_nodes"]], "dfs_successors() (in module networkx.algorithms.traversal.depth_first_search)": [[715, "networkx.algorithms.traversal.depth_first_search.dfs_successors"]], "dfs_tree() (in module networkx.algorithms.traversal.depth_first_search)": [[716, "networkx.algorithms.traversal.depth_first_search.dfs_tree"]], "edge_bfs() (in module networkx.algorithms.traversal.edgebfs)": [[717, "networkx.algorithms.traversal.edgebfs.edge_bfs"]], "edge_dfs() (in module networkx.algorithms.traversal.edgedfs)": [[718, "networkx.algorithms.traversal.edgedfs.edge_dfs"]], "arborescenceiterator (class in networkx.algorithms.tree.branchings)": [[719, "networkx.algorithms.tree.branchings.ArborescenceIterator"]], "__init__() (arborescenceiterator method)": [[719, "networkx.algorithms.tree.branchings.ArborescenceIterator.__init__"]], "edmonds (class in networkx.algorithms.tree.branchings)": [[720, "networkx.algorithms.tree.branchings.Edmonds"]], "__init__() (edmonds method)": [[720, "networkx.algorithms.tree.branchings.Edmonds.__init__"]], "branching_weight() (in module networkx.algorithms.tree.branchings)": [[721, "networkx.algorithms.tree.branchings.branching_weight"]], "greedy_branching() (in module networkx.algorithms.tree.branchings)": [[722, "networkx.algorithms.tree.branchings.greedy_branching"]], "maximum_branching() (in module networkx.algorithms.tree.branchings)": [[723, "networkx.algorithms.tree.branchings.maximum_branching"]], "maximum_spanning_arborescence() (in module networkx.algorithms.tree.branchings)": [[724, "networkx.algorithms.tree.branchings.maximum_spanning_arborescence"]], "minimum_branching() (in module networkx.algorithms.tree.branchings)": [[725, "networkx.algorithms.tree.branchings.minimum_branching"]], "minimum_spanning_arborescence() (in module networkx.algorithms.tree.branchings)": [[726, "networkx.algorithms.tree.branchings.minimum_spanning_arborescence"]], "notatree": [[727, "networkx.algorithms.tree.coding.NotATree"]], "from_nested_tuple() (in module networkx.algorithms.tree.coding)": [[728, "networkx.algorithms.tree.coding.from_nested_tuple"]], "from_prufer_sequence() (in module networkx.algorithms.tree.coding)": [[729, "networkx.algorithms.tree.coding.from_prufer_sequence"]], "to_nested_tuple() (in module networkx.algorithms.tree.coding)": [[730, "networkx.algorithms.tree.coding.to_nested_tuple"]], "to_prufer_sequence() (in module networkx.algorithms.tree.coding)": [[731, "networkx.algorithms.tree.coding.to_prufer_sequence"]], "junction_tree() (in module networkx.algorithms.tree.decomposition)": [[732, "networkx.algorithms.tree.decomposition.junction_tree"]], "spanningtreeiterator (class in networkx.algorithms.tree.mst)": [[733, "networkx.algorithms.tree.mst.SpanningTreeIterator"]], "__init__() (spanningtreeiterator method)": [[733, "networkx.algorithms.tree.mst.SpanningTreeIterator.__init__"]], "maximum_spanning_edges() (in module networkx.algorithms.tree.mst)": [[734, "networkx.algorithms.tree.mst.maximum_spanning_edges"]], "maximum_spanning_tree() (in module networkx.algorithms.tree.mst)": [[735, "networkx.algorithms.tree.mst.maximum_spanning_tree"]], "minimum_spanning_edges() (in module networkx.algorithms.tree.mst)": [[736, "networkx.algorithms.tree.mst.minimum_spanning_edges"]], "minimum_spanning_tree() (in module networkx.algorithms.tree.mst)": [[737, "networkx.algorithms.tree.mst.minimum_spanning_tree"]], "random_spanning_tree() (in module networkx.algorithms.tree.mst)": [[738, 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"order() (multigraph method)": [[1001, "networkx.MultiGraph.order"]], "remove_edge() (multigraph method)": [[1002, "networkx.MultiGraph.remove_edge"]], "remove_edges_from() (multigraph method)": [[1003, "networkx.MultiGraph.remove_edges_from"]], "remove_node() (multigraph method)": [[1004, "networkx.MultiGraph.remove_node"]], "remove_nodes_from() (multigraph method)": [[1005, "networkx.MultiGraph.remove_nodes_from"]], "size() (multigraph method)": [[1006, "networkx.MultiGraph.size"]], "subgraph() (multigraph method)": [[1007, "networkx.MultiGraph.subgraph"]], "to_directed() (multigraph method)": [[1008, "networkx.MultiGraph.to_directed"]], "to_undirected() (multigraph method)": [[1009, "networkx.MultiGraph.to_undirected"]], "update() (multigraph method)": [[1010, "networkx.MultiGraph.update"]], "_dispatch() (in module networkx.classes.backends)": [[1011, "networkx.classes.backends._dispatch"]], "adjacencyview (class in networkx.classes.coreviews)": [[1012, "networkx.classes.coreviews.AdjacencyView"]], "__init__() (adjacencyview method)": [[1012, "networkx.classes.coreviews.AdjacencyView.__init__"]], "atlasview (class in networkx.classes.coreviews)": [[1013, "networkx.classes.coreviews.AtlasView"]], "__init__() (atlasview method)": [[1013, "networkx.classes.coreviews.AtlasView.__init__"]], "filteradjacency (class in networkx.classes.coreviews)": [[1014, "networkx.classes.coreviews.FilterAdjacency"]], "__init__() (filteradjacency method)": [[1014, "networkx.classes.coreviews.FilterAdjacency.__init__"]], "filteratlas (class in networkx.classes.coreviews)": [[1015, "networkx.classes.coreviews.FilterAtlas"]], "__init__() (filteratlas method)": [[1015, "networkx.classes.coreviews.FilterAtlas.__init__"]], "filtermultiadjacency (class in networkx.classes.coreviews)": [[1016, "networkx.classes.coreviews.FilterMultiAdjacency"]], "__init__() (filtermultiadjacency method)": [[1016, "networkx.classes.coreviews.FilterMultiAdjacency.__init__"]], "filtermultiinner (class in networkx.classes.coreviews)": [[1017, "networkx.classes.coreviews.FilterMultiInner"]], "__init__() (filtermultiinner method)": [[1017, "networkx.classes.coreviews.FilterMultiInner.__init__"]], "multiadjacencyview (class in networkx.classes.coreviews)": [[1018, "networkx.classes.coreviews.MultiAdjacencyView"]], "__init__() (multiadjacencyview method)": [[1018, "networkx.classes.coreviews.MultiAdjacencyView.__init__"]], "unionadjacency (class in networkx.classes.coreviews)": [[1019, "networkx.classes.coreviews.UnionAdjacency"]], "__init__() (unionadjacency method)": [[1019, "networkx.classes.coreviews.UnionAdjacency.__init__"]], "unionatlas (class in networkx.classes.coreviews)": [[1020, "networkx.classes.coreviews.UnionAtlas"]], "__init__() (unionatlas method)": [[1020, "networkx.classes.coreviews.UnionAtlas.__init__"]], "unionmultiadjacency (class in networkx.classes.coreviews)": [[1021, "networkx.classes.coreviews.UnionMultiAdjacency"]], "__init__() 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networkx.classes.filters)": [[1029, "networkx.classes.filters.show_diedges"]], "show_edges() (in module networkx.classes.filters)": [[1030, "networkx.classes.filters.show_edges"]], "show_multidiedges() (in module networkx.classes.filters)": [[1031, "networkx.classes.filters.show_multidiedges"]], "show_multiedges() (in module networkx.classes.filters)": [[1032, "networkx.classes.filters.show_multiedges"]], "__init__() (show_nodes method)": [[1033, "networkx.classes.filters.show_nodes.__init__"]], "show_nodes (class in networkx.classes.filters)": [[1033, "networkx.classes.filters.show_nodes"]], "generic_graph_view() (in module networkx.classes.graphviews)": [[1034, "networkx.classes.graphviews.generic_graph_view"]], "reverse_view() (in module networkx.classes.graphviews)": [[1035, "networkx.classes.graphviews.reverse_view"]], "subgraph_view() (in module networkx.classes.graphviews)": [[1036, "networkx.classes.graphviews.subgraph_view"]], "graph (class in networkx)": [[1037, "networkx.Graph"]], "networkx.classes.backends": [[1038, "module-networkx.classes.backends"]], "networkx.classes.coreviews": [[1038, "module-networkx.classes.coreviews"]], "networkx.classes.filters": [[1038, "module-networkx.classes.filters"]], "networkx.classes.graphviews": [[1038, "module-networkx.classes.graphviews"]], "multidigraph (class in networkx)": [[1039, "networkx.MultiDiGraph"]], "multigraph (class in networkx)": [[1040, "networkx.MultiGraph"]], "networkx.convert": [[1041, "module-networkx.convert"]], "networkx.convert_matrix": [[1041, "module-networkx.convert_matrix"]], "networkx.drawing.layout": [[1042, "module-networkx.drawing.layout"]], "networkx.drawing.nx_agraph": [[1042, "module-networkx.drawing.nx_agraph"]], "networkx.drawing.nx_pydot": [[1042, "module-networkx.drawing.nx_pydot"]], "networkx.drawing.nx_pylab": [[1042, "module-networkx.drawing.nx_pylab"]], "ambiguoussolution (class in networkx)": [[1043, "networkx.AmbiguousSolution"]], "exceededmaxiterations (class in networkx)": [[1043, "networkx.ExceededMaxIterations"]], "hasacycle (class in networkx)": [[1043, "networkx.HasACycle"]], "networkxalgorithmerror (class in networkx)": [[1043, "networkx.NetworkXAlgorithmError"]], "networkxerror (class in networkx)": [[1043, "networkx.NetworkXError"]], "networkxexception (class in networkx)": [[1043, "networkx.NetworkXException"]], "networkxnocycle (class in networkx)": [[1043, "networkx.NetworkXNoCycle"]], "networkxnopath (class in networkx)": [[1043, "networkx.NetworkXNoPath"]], "networkxnotimplemented (class in networkx)": [[1043, "networkx.NetworkXNotImplemented"]], "networkxpointlessconcept (class in networkx)": [[1043, "networkx.NetworkXPointlessConcept"]], "networkxunbounded (class in networkx)": [[1043, "networkx.NetworkXUnbounded"]], "networkxunfeasible (class in networkx)": [[1043, "networkx.NetworkXUnfeasible"]], "nodenotfound (class in networkx)": [[1043, "networkx.NodeNotFound"]], "poweriterationfailedconvergence (class in networkx)": [[1043, "networkx.PowerIterationFailedConvergence"]], "networkx.exception": [[1043, "module-networkx.exception"]], "networkx.classes.function": [[1044, "module-networkx.classes.function"]], "assemble() (argmap method)": [[1045, "networkx.utils.decorators.argmap.assemble"]], "compile() (argmap method)": [[1046, "networkx.utils.decorators.argmap.compile"]], "signature() (argmap class method)": [[1047, "networkx.utils.decorators.argmap.signature"]], "pop() (mappedqueue method)": [[1048, "networkx.utils.mapped_queue.MappedQueue.pop"]], "push() (mappedqueue method)": [[1049, "networkx.utils.mapped_queue.MappedQueue.push"]], "remove() (mappedqueue method)": [[1050, "networkx.utils.mapped_queue.MappedQueue.remove"]], "update() (mappedqueue method)": [[1051, "networkx.utils.mapped_queue.MappedQueue.update"]], "add_cycle() (in module networkx.classes.function)": [[1052, "networkx.classes.function.add_cycle"]], "add_path() (in module networkx.classes.function)": [[1053, "networkx.classes.function.add_path"]], "add_star() (in module networkx.classes.function)": [[1054, "networkx.classes.function.add_star"]], "all_neighbors() (in module networkx.classes.function)": [[1055, "networkx.classes.function.all_neighbors"]], "common_neighbors() (in module networkx.classes.function)": [[1056, "networkx.classes.function.common_neighbors"]], "create_empty_copy() (in module networkx.classes.function)": [[1057, "networkx.classes.function.create_empty_copy"]], "degree() (in module networkx.classes.function)": [[1058, "networkx.classes.function.degree"]], "degree_histogram() (in module networkx.classes.function)": [[1059, "networkx.classes.function.degree_histogram"]], "density() (in module networkx.classes.function)": [[1060, "networkx.classes.function.density"]], "edge_subgraph() (in module networkx.classes.function)": [[1061, "networkx.classes.function.edge_subgraph"]], "edges() (in module networkx.classes.function)": [[1062, "networkx.classes.function.edges"]], "freeze() (in module networkx.classes.function)": [[1063, "networkx.classes.function.freeze"]], "get_edge_attributes() (in module networkx.classes.function)": [[1064, "networkx.classes.function.get_edge_attributes"]], "get_node_attributes() (in module networkx.classes.function)": [[1065, "networkx.classes.function.get_node_attributes"]], "induced_subgraph() (in module networkx.classes.function)": [[1066, "networkx.classes.function.induced_subgraph"]], "is_directed() (in module networkx.classes.function)": [[1067, "networkx.classes.function.is_directed"]], "is_empty() (in module networkx.classes.function)": [[1068, "networkx.classes.function.is_empty"]], "is_frozen() (in module networkx.classes.function)": [[1069, "networkx.classes.function.is_frozen"]], "is_negatively_weighted() (in module networkx.classes.function)": [[1070, "networkx.classes.function.is_negatively_weighted"]], "is_path() (in module networkx.classes.function)": [[1071, "networkx.classes.function.is_path"]], "is_weighted() (in module networkx.classes.function)": [[1072, "networkx.classes.function.is_weighted"]], "neighbors() (in module networkx.classes.function)": [[1073, "networkx.classes.function.neighbors"]], "nodes() (in module networkx.classes.function)": [[1074, "networkx.classes.function.nodes"]], "nodes_with_selfloops() (in module networkx.classes.function)": [[1075, "networkx.classes.function.nodes_with_selfloops"]], "non_edges() (in module networkx.classes.function)": [[1076, "networkx.classes.function.non_edges"]], "non_neighbors() (in module networkx.classes.function)": [[1077, "networkx.classes.function.non_neighbors"]], "number_of_edges() (in module networkx.classes.function)": [[1078, "networkx.classes.function.number_of_edges"]], "number_of_nodes() (in module networkx.classes.function)": [[1079, "networkx.classes.function.number_of_nodes"]], "number_of_selfloops() (in module networkx.classes.function)": [[1080, "networkx.classes.function.number_of_selfloops"]], "path_weight() (in module networkx.classes.function)": [[1081, "networkx.classes.function.path_weight"]], "restricted_view() (in module networkx.classes.function)": [[1082, "networkx.classes.function.restricted_view"]], "reverse_view() (in module networkx.classes.function)": [[1083, "networkx.classes.function.reverse_view"]], "selfloop_edges() (in module networkx.classes.function)": [[1084, "networkx.classes.function.selfloop_edges"]], "set_edge_attributes() (in module networkx.classes.function)": [[1085, "networkx.classes.function.set_edge_attributes"]], "set_node_attributes() (in module networkx.classes.function)": [[1086, "networkx.classes.function.set_node_attributes"]], "subgraph() (in module networkx.classes.function)": [[1087, "networkx.classes.function.subgraph"]], "subgraph_view() (in module networkx.classes.function)": [[1088, "networkx.classes.function.subgraph_view"]], "to_directed() (in module networkx.classes.function)": [[1089, "networkx.classes.function.to_directed"]], "to_undirected() (in module networkx.classes.function)": [[1090, "networkx.classes.function.to_undirected"]], "from_dict_of_dicts() (in module networkx.convert)": [[1091, "networkx.convert.from_dict_of_dicts"]], "from_dict_of_lists() (in module networkx.convert)": [[1092, "networkx.convert.from_dict_of_lists"]], "from_edgelist() (in module networkx.convert)": [[1093, "networkx.convert.from_edgelist"]], "to_dict_of_dicts() (in module networkx.convert)": [[1094, "networkx.convert.to_dict_of_dicts"]], "to_dict_of_lists() (in module networkx.convert)": [[1095, "networkx.convert.to_dict_of_lists"]], "to_edgelist() (in module networkx.convert)": [[1096, "networkx.convert.to_edgelist"]], "to_networkx_graph() (in module networkx.convert)": [[1097, "networkx.convert.to_networkx_graph"]], "from_numpy_array() (in module networkx.convert_matrix)": [[1098, "networkx.convert_matrix.from_numpy_array"]], "from_pandas_adjacency() (in module networkx.convert_matrix)": [[1099, "networkx.convert_matrix.from_pandas_adjacency"]], "from_pandas_edgelist() (in module networkx.convert_matrix)": [[1100, "networkx.convert_matrix.from_pandas_edgelist"]], "from_scipy_sparse_array() (in module networkx.convert_matrix)": [[1101, "networkx.convert_matrix.from_scipy_sparse_array"]], "to_numpy_array() (in module networkx.convert_matrix)": [[1102, "networkx.convert_matrix.to_numpy_array"]], "to_pandas_adjacency() (in module networkx.convert_matrix)": [[1103, "networkx.convert_matrix.to_pandas_adjacency"]], "to_pandas_edgelist() (in module networkx.convert_matrix)": [[1104, "networkx.convert_matrix.to_pandas_edgelist"]], "to_scipy_sparse_array() (in module networkx.convert_matrix)": [[1105, "networkx.convert_matrix.to_scipy_sparse_array"]], "bipartite_layout() (in module networkx.drawing.layout)": [[1106, "networkx.drawing.layout.bipartite_layout"]], "circular_layout() (in module networkx.drawing.layout)": [[1107, "networkx.drawing.layout.circular_layout"]], "kamada_kawai_layout() (in module networkx.drawing.layout)": [[1108, "networkx.drawing.layout.kamada_kawai_layout"]], "multipartite_layout() (in module networkx.drawing.layout)": [[1109, "networkx.drawing.layout.multipartite_layout"]], "planar_layout() (in module networkx.drawing.layout)": [[1110, "networkx.drawing.layout.planar_layout"]], "random_layout() (in module networkx.drawing.layout)": [[1111, "networkx.drawing.layout.random_layout"]], "rescale_layout() (in module networkx.drawing.layout)": [[1112, "networkx.drawing.layout.rescale_layout"]], "rescale_layout_dict() (in module networkx.drawing.layout)": [[1113, "networkx.drawing.layout.rescale_layout_dict"]], "shell_layout() (in module networkx.drawing.layout)": [[1114, "networkx.drawing.layout.shell_layout"]], "spectral_layout() (in module networkx.drawing.layout)": [[1115, "networkx.drawing.layout.spectral_layout"]], "spiral_layout() (in module networkx.drawing.layout)": [[1116, "networkx.drawing.layout.spiral_layout"]], "spring_layout() (in module networkx.drawing.layout)": [[1117, "networkx.drawing.layout.spring_layout"]], "from_agraph() (in module networkx.drawing.nx_agraph)": [[1118, "networkx.drawing.nx_agraph.from_agraph"]], "graphviz_layout() (in module networkx.drawing.nx_agraph)": [[1119, "networkx.drawing.nx_agraph.graphviz_layout"]], "pygraphviz_layout() (in module networkx.drawing.nx_agraph)": [[1120, "networkx.drawing.nx_agraph.pygraphviz_layout"]], "read_dot() (in module networkx.drawing.nx_agraph)": [[1121, "networkx.drawing.nx_agraph.read_dot"]], "to_agraph() (in module networkx.drawing.nx_agraph)": [[1122, "networkx.drawing.nx_agraph.to_agraph"]], "write_dot() (in module networkx.drawing.nx_agraph)": [[1123, "networkx.drawing.nx_agraph.write_dot"]], "from_pydot() (in module networkx.drawing.nx_pydot)": [[1124, "networkx.drawing.nx_pydot.from_pydot"]], "graphviz_layout() (in module networkx.drawing.nx_pydot)": [[1125, "networkx.drawing.nx_pydot.graphviz_layout"]], "pydot_layout() (in module networkx.drawing.nx_pydot)": [[1126, "networkx.drawing.nx_pydot.pydot_layout"]], "read_dot() (in module networkx.drawing.nx_pydot)": [[1127, "networkx.drawing.nx_pydot.read_dot"]], "to_pydot() (in module networkx.drawing.nx_pydot)": [[1128, "networkx.drawing.nx_pydot.to_pydot"]], "write_dot() (in module networkx.drawing.nx_pydot)": [[1129, "networkx.drawing.nx_pydot.write_dot"]], "draw() (in module networkx.drawing.nx_pylab)": [[1130, "networkx.drawing.nx_pylab.draw"]], "draw_circular() (in module networkx.drawing.nx_pylab)": [[1131, "networkx.drawing.nx_pylab.draw_circular"]], "draw_kamada_kawai() (in module networkx.drawing.nx_pylab)": [[1132, "networkx.drawing.nx_pylab.draw_kamada_kawai"]], "draw_networkx() (in module networkx.drawing.nx_pylab)": [[1133, "networkx.drawing.nx_pylab.draw_networkx"]], "draw_networkx_edge_labels() (in module networkx.drawing.nx_pylab)": [[1134, "networkx.drawing.nx_pylab.draw_networkx_edge_labels"]], "draw_networkx_edges() (in module networkx.drawing.nx_pylab)": [[1135, "networkx.drawing.nx_pylab.draw_networkx_edges"]], "draw_networkx_labels() (in module networkx.drawing.nx_pylab)": [[1136, "networkx.drawing.nx_pylab.draw_networkx_labels"]], "draw_networkx_nodes() (in module networkx.drawing.nx_pylab)": [[1137, "networkx.drawing.nx_pylab.draw_networkx_nodes"]], "draw_planar() (in module networkx.drawing.nx_pylab)": [[1138, "networkx.drawing.nx_pylab.draw_planar"]], "draw_random() (in module networkx.drawing.nx_pylab)": [[1139, "networkx.drawing.nx_pylab.draw_random"]], "draw_shell() (in module networkx.drawing.nx_pylab)": [[1140, "networkx.drawing.nx_pylab.draw_shell"]], "draw_spectral() (in module networkx.drawing.nx_pylab)": [[1141, "networkx.drawing.nx_pylab.draw_spectral"]], "draw_spring() (in module networkx.drawing.nx_pylab)": [[1142, "networkx.drawing.nx_pylab.draw_spring"]], "graph_atlas() (in module networkx.generators.atlas)": [[1143, "networkx.generators.atlas.graph_atlas"]], "graph_atlas_g() (in module networkx.generators.atlas)": [[1144, "networkx.generators.atlas.graph_atlas_g"]], "balanced_tree() (in module networkx.generators.classic)": [[1145, "networkx.generators.classic.balanced_tree"]], "barbell_graph() (in module networkx.generators.classic)": [[1146, "networkx.generators.classic.barbell_graph"]], "binomial_tree() (in module networkx.generators.classic)": [[1147, "networkx.generators.classic.binomial_tree"]], "circulant_graph() (in module networkx.generators.classic)": [[1148, "networkx.generators.classic.circulant_graph"]], "circular_ladder_graph() (in module networkx.generators.classic)": [[1149, "networkx.generators.classic.circular_ladder_graph"]], "complete_graph() (in module networkx.generators.classic)": [[1150, "networkx.generators.classic.complete_graph"]], "complete_multipartite_graph() (in module networkx.generators.classic)": [[1151, "networkx.generators.classic.complete_multipartite_graph"]], "cycle_graph() (in module networkx.generators.classic)": [[1152, "networkx.generators.classic.cycle_graph"]], "dorogovtsev_goltsev_mendes_graph() (in module networkx.generators.classic)": [[1153, "networkx.generators.classic.dorogovtsev_goltsev_mendes_graph"]], "empty_graph() (in module networkx.generators.classic)": [[1154, "networkx.generators.classic.empty_graph"]], "full_rary_tree() (in module networkx.generators.classic)": [[1155, "networkx.generators.classic.full_rary_tree"]], "ladder_graph() (in module networkx.generators.classic)": [[1156, "networkx.generators.classic.ladder_graph"]], "lollipop_graph() (in module networkx.generators.classic)": [[1157, "networkx.generators.classic.lollipop_graph"]], "null_graph() (in module networkx.generators.classic)": [[1158, "networkx.generators.classic.null_graph"]], "path_graph() (in module networkx.generators.classic)": [[1159, "networkx.generators.classic.path_graph"]], "star_graph() (in module networkx.generators.classic)": [[1160, "networkx.generators.classic.star_graph"]], "trivial_graph() (in module networkx.generators.classic)": [[1161, "networkx.generators.classic.trivial_graph"]], "turan_graph() (in module networkx.generators.classic)": [[1162, "networkx.generators.classic.turan_graph"]], "wheel_graph() (in module networkx.generators.classic)": [[1163, "networkx.generators.classic.wheel_graph"]], "random_cograph() (in module networkx.generators.cographs)": [[1164, "networkx.generators.cographs.random_cograph"]], "lfr_benchmark_graph() (in module networkx.generators.community)": [[1165, "networkx.generators.community.LFR_benchmark_graph"]], "caveman_graph() (in module networkx.generators.community)": [[1166, "networkx.generators.community.caveman_graph"]], "connected_caveman_graph() (in module networkx.generators.community)": [[1167, "networkx.generators.community.connected_caveman_graph"]], "gaussian_random_partition_graph() (in module networkx.generators.community)": [[1168, "networkx.generators.community.gaussian_random_partition_graph"]], "planted_partition_graph() (in module networkx.generators.community)": [[1169, "networkx.generators.community.planted_partition_graph"]], "random_partition_graph() (in module networkx.generators.community)": [[1170, "networkx.generators.community.random_partition_graph"]], "relaxed_caveman_graph() (in module networkx.generators.community)": [[1171, "networkx.generators.community.relaxed_caveman_graph"]], "ring_of_cliques() (in module networkx.generators.community)": [[1172, "networkx.generators.community.ring_of_cliques"]], "stochastic_block_model() (in module networkx.generators.community)": [[1173, "networkx.generators.community.stochastic_block_model"]], "windmill_graph() (in module networkx.generators.community)": [[1174, "networkx.generators.community.windmill_graph"]], "configuration_model() (in module networkx.generators.degree_seq)": [[1175, "networkx.generators.degree_seq.configuration_model"]], "degree_sequence_tree() (in module networkx.generators.degree_seq)": [[1176, "networkx.generators.degree_seq.degree_sequence_tree"]], "directed_configuration_model() (in module networkx.generators.degree_seq)": [[1177, "networkx.generators.degree_seq.directed_configuration_model"]], "directed_havel_hakimi_graph() (in module networkx.generators.degree_seq)": [[1178, "networkx.generators.degree_seq.directed_havel_hakimi_graph"]], "expected_degree_graph() (in module networkx.generators.degree_seq)": [[1179, "networkx.generators.degree_seq.expected_degree_graph"]], "havel_hakimi_graph() (in module networkx.generators.degree_seq)": [[1180, "networkx.generators.degree_seq.havel_hakimi_graph"]], "random_degree_sequence_graph() (in module networkx.generators.degree_seq)": [[1181, "networkx.generators.degree_seq.random_degree_sequence_graph"]], "gn_graph() (in module networkx.generators.directed)": [[1182, "networkx.generators.directed.gn_graph"]], "gnc_graph() (in module networkx.generators.directed)": [[1183, "networkx.generators.directed.gnc_graph"]], "gnr_graph() (in module networkx.generators.directed)": [[1184, "networkx.generators.directed.gnr_graph"]], "random_k_out_graph() (in module networkx.generators.directed)": [[1185, "networkx.generators.directed.random_k_out_graph"]], "scale_free_graph() (in module networkx.generators.directed)": [[1186, "networkx.generators.directed.scale_free_graph"]], "duplication_divergence_graph() (in module networkx.generators.duplication)": [[1187, "networkx.generators.duplication.duplication_divergence_graph"]], "partial_duplication_graph() (in module networkx.generators.duplication)": [[1188, "networkx.generators.duplication.partial_duplication_graph"]], "ego_graph() (in module networkx.generators.ego)": [[1189, "networkx.generators.ego.ego_graph"]], "chordal_cycle_graph() (in module networkx.generators.expanders)": [[1190, "networkx.generators.expanders.chordal_cycle_graph"]], "margulis_gabber_galil_graph() (in module networkx.generators.expanders)": [[1191, "networkx.generators.expanders.margulis_gabber_galil_graph"]], "paley_graph() (in module networkx.generators.expanders)": [[1192, "networkx.generators.expanders.paley_graph"]], "geographical_threshold_graph() (in module networkx.generators.geometric)": [[1193, "networkx.generators.geometric.geographical_threshold_graph"]], "geometric_edges() (in module networkx.generators.geometric)": [[1194, "networkx.generators.geometric.geometric_edges"]], "navigable_small_world_graph() (in module networkx.generators.geometric)": [[1195, "networkx.generators.geometric.navigable_small_world_graph"]], "random_geometric_graph() (in module networkx.generators.geometric)": [[1196, "networkx.generators.geometric.random_geometric_graph"]], "soft_random_geometric_graph() (in module networkx.generators.geometric)": [[1197, "networkx.generators.geometric.soft_random_geometric_graph"]], "thresholded_random_geometric_graph() (in module networkx.generators.geometric)": [[1198, "networkx.generators.geometric.thresholded_random_geometric_graph"]], "waxman_graph() (in module networkx.generators.geometric)": [[1199, "networkx.generators.geometric.waxman_graph"]], "hkn_harary_graph() (in module networkx.generators.harary_graph)": [[1200, "networkx.generators.harary_graph.hkn_harary_graph"]], "hnm_harary_graph() (in module networkx.generators.harary_graph)": [[1201, "networkx.generators.harary_graph.hnm_harary_graph"]], "random_internet_as_graph() (in module networkx.generators.internet_as_graphs)": [[1202, "networkx.generators.internet_as_graphs.random_internet_as_graph"]], "general_random_intersection_graph() (in module networkx.generators.intersection)": [[1203, "networkx.generators.intersection.general_random_intersection_graph"]], "k_random_intersection_graph() (in module networkx.generators.intersection)": [[1204, "networkx.generators.intersection.k_random_intersection_graph"]], "uniform_random_intersection_graph() (in module networkx.generators.intersection)": [[1205, "networkx.generators.intersection.uniform_random_intersection_graph"]], "interval_graph() (in module networkx.generators.interval_graph)": [[1206, "networkx.generators.interval_graph.interval_graph"]], "directed_joint_degree_graph() (in module networkx.generators.joint_degree_seq)": [[1207, "networkx.generators.joint_degree_seq.directed_joint_degree_graph"]], "is_valid_directed_joint_degree() (in module networkx.generators.joint_degree_seq)": [[1208, "networkx.generators.joint_degree_seq.is_valid_directed_joint_degree"]], "is_valid_joint_degree() (in module networkx.generators.joint_degree_seq)": [[1209, "networkx.generators.joint_degree_seq.is_valid_joint_degree"]], "joint_degree_graph() (in module networkx.generators.joint_degree_seq)": [[1210, "networkx.generators.joint_degree_seq.joint_degree_graph"]], "grid_2d_graph() (in module networkx.generators.lattice)": [[1211, "networkx.generators.lattice.grid_2d_graph"]], "grid_graph() (in module networkx.generators.lattice)": [[1212, "networkx.generators.lattice.grid_graph"]], "hexagonal_lattice_graph() (in module networkx.generators.lattice)": [[1213, "networkx.generators.lattice.hexagonal_lattice_graph"]], "hypercube_graph() (in module networkx.generators.lattice)": [[1214, "networkx.generators.lattice.hypercube_graph"]], "triangular_lattice_graph() (in module networkx.generators.lattice)": [[1215, "networkx.generators.lattice.triangular_lattice_graph"]], "inverse_line_graph() (in module networkx.generators.line)": [[1216, "networkx.generators.line.inverse_line_graph"]], "line_graph() (in module networkx.generators.line)": [[1217, "networkx.generators.line.line_graph"]], "mycielski_graph() (in module networkx.generators.mycielski)": [[1218, "networkx.generators.mycielski.mycielski_graph"]], "mycielskian() (in module networkx.generators.mycielski)": [[1219, "networkx.generators.mycielski.mycielskian"]], "nonisomorphic_trees() (in module networkx.generators.nonisomorphic_trees)": [[1220, "networkx.generators.nonisomorphic_trees.nonisomorphic_trees"]], "number_of_nonisomorphic_trees() (in module networkx.generators.nonisomorphic_trees)": [[1221, "networkx.generators.nonisomorphic_trees.number_of_nonisomorphic_trees"]], "random_clustered_graph() (in module networkx.generators.random_clustered)": [[1222, "networkx.generators.random_clustered.random_clustered_graph"]], "barabasi_albert_graph() (in module networkx.generators.random_graphs)": [[1223, "networkx.generators.random_graphs.barabasi_albert_graph"]], "binomial_graph() (in module networkx.generators.random_graphs)": [[1224, "networkx.generators.random_graphs.binomial_graph"]], "connected_watts_strogatz_graph() (in module networkx.generators.random_graphs)": [[1225, "networkx.generators.random_graphs.connected_watts_strogatz_graph"]], "dense_gnm_random_graph() (in module networkx.generators.random_graphs)": [[1226, "networkx.generators.random_graphs.dense_gnm_random_graph"]], "dual_barabasi_albert_graph() (in module networkx.generators.random_graphs)": [[1227, "networkx.generators.random_graphs.dual_barabasi_albert_graph"]], "erdos_renyi_graph() (in module networkx.generators.random_graphs)": [[1228, "networkx.generators.random_graphs.erdos_renyi_graph"]], "extended_barabasi_albert_graph() (in module networkx.generators.random_graphs)": [[1229, "networkx.generators.random_graphs.extended_barabasi_albert_graph"]], "fast_gnp_random_graph() (in module networkx.generators.random_graphs)": [[1230, "networkx.generators.random_graphs.fast_gnp_random_graph"]], "gnm_random_graph() (in module networkx.generators.random_graphs)": [[1231, "networkx.generators.random_graphs.gnm_random_graph"]], "gnp_random_graph() (in module networkx.generators.random_graphs)": [[1232, "networkx.generators.random_graphs.gnp_random_graph"]], "newman_watts_strogatz_graph() (in module networkx.generators.random_graphs)": [[1233, "networkx.generators.random_graphs.newman_watts_strogatz_graph"]], "powerlaw_cluster_graph() (in module networkx.generators.random_graphs)": [[1234, "networkx.generators.random_graphs.powerlaw_cluster_graph"]], "random_kernel_graph() (in module networkx.generators.random_graphs)": [[1235, "networkx.generators.random_graphs.random_kernel_graph"]], "random_lobster() (in module networkx.generators.random_graphs)": [[1236, "networkx.generators.random_graphs.random_lobster"]], "random_powerlaw_tree() (in module networkx.generators.random_graphs)": [[1237, "networkx.generators.random_graphs.random_powerlaw_tree"]], "random_powerlaw_tree_sequence() (in module networkx.generators.random_graphs)": [[1238, "networkx.generators.random_graphs.random_powerlaw_tree_sequence"]], "random_regular_graph() (in module networkx.generators.random_graphs)": [[1239, "networkx.generators.random_graphs.random_regular_graph"]], "random_shell_graph() (in module networkx.generators.random_graphs)": [[1240, "networkx.generators.random_graphs.random_shell_graph"]], "watts_strogatz_graph() (in module networkx.generators.random_graphs)": [[1241, "networkx.generators.random_graphs.watts_strogatz_graph"]], "lcf_graph() (in module networkx.generators.small)": [[1242, "networkx.generators.small.LCF_graph"]], "bull_graph() (in module networkx.generators.small)": [[1243, "networkx.generators.small.bull_graph"]], "chvatal_graph() (in module networkx.generators.small)": [[1244, "networkx.generators.small.chvatal_graph"]], "cubical_graph() (in module networkx.generators.small)": [[1245, "networkx.generators.small.cubical_graph"]], "desargues_graph() (in module networkx.generators.small)": [[1246, "networkx.generators.small.desargues_graph"]], "diamond_graph() (in module networkx.generators.small)": [[1247, "networkx.generators.small.diamond_graph"]], "dodecahedral_graph() (in module networkx.generators.small)": [[1248, "networkx.generators.small.dodecahedral_graph"]], "frucht_graph() (in module networkx.generators.small)": [[1249, "networkx.generators.small.frucht_graph"]], "heawood_graph() (in module networkx.generators.small)": [[1250, "networkx.generators.small.heawood_graph"]], "hoffman_singleton_graph() (in module networkx.generators.small)": [[1251, "networkx.generators.small.hoffman_singleton_graph"]], "house_graph() (in module networkx.generators.small)": [[1252, "networkx.generators.small.house_graph"]], "house_x_graph() (in module networkx.generators.small)": [[1253, "networkx.generators.small.house_x_graph"]], "icosahedral_graph() (in module networkx.generators.small)": [[1254, "networkx.generators.small.icosahedral_graph"]], "krackhardt_kite_graph() (in module networkx.generators.small)": [[1255, "networkx.generators.small.krackhardt_kite_graph"]], "moebius_kantor_graph() (in module networkx.generators.small)": [[1256, "networkx.generators.small.moebius_kantor_graph"]], "octahedral_graph() (in module networkx.generators.small)": [[1257, "networkx.generators.small.octahedral_graph"]], "pappus_graph() (in module networkx.generators.small)": [[1258, "networkx.generators.small.pappus_graph"]], "petersen_graph() (in module networkx.generators.small)": [[1259, "networkx.generators.small.petersen_graph"]], "sedgewick_maze_graph() (in module networkx.generators.small)": [[1260, "networkx.generators.small.sedgewick_maze_graph"]], "tetrahedral_graph() (in module networkx.generators.small)": [[1261, "networkx.generators.small.tetrahedral_graph"]], "truncated_cube_graph() (in module networkx.generators.small)": [[1262, "networkx.generators.small.truncated_cube_graph"]], "truncated_tetrahedron_graph() (in module networkx.generators.small)": [[1263, "networkx.generators.small.truncated_tetrahedron_graph"]], "tutte_graph() (in module networkx.generators.small)": [[1264, "networkx.generators.small.tutte_graph"]], "davis_southern_women_graph() (in module networkx.generators.social)": [[1265, "networkx.generators.social.davis_southern_women_graph"]], "florentine_families_graph() (in module networkx.generators.social)": [[1266, "networkx.generators.social.florentine_families_graph"]], "karate_club_graph() (in module networkx.generators.social)": [[1267, "networkx.generators.social.karate_club_graph"]], "les_miserables_graph() (in module networkx.generators.social)": [[1268, "networkx.generators.social.les_miserables_graph"]], "spectral_graph_forge() (in module networkx.generators.spectral_graph_forge)": [[1269, "networkx.generators.spectral_graph_forge.spectral_graph_forge"]], "stochastic_graph() (in module networkx.generators.stochastic)": [[1270, "networkx.generators.stochastic.stochastic_graph"]], "sudoku_graph() (in module networkx.generators.sudoku)": [[1271, "networkx.generators.sudoku.sudoku_graph"]], "prefix_tree() (in module networkx.generators.trees)": [[1272, "networkx.generators.trees.prefix_tree"]], "random_tree() (in module networkx.generators.trees)": [[1273, "networkx.generators.trees.random_tree"]], "triad_graph() (in module networkx.generators.triads)": [[1274, "networkx.generators.triads.triad_graph"]], "algebraic_connectivity() (in module networkx.linalg.algebraicconnectivity)": [[1275, "networkx.linalg.algebraicconnectivity.algebraic_connectivity"]], "fiedler_vector() (in module networkx.linalg.algebraicconnectivity)": [[1276, "networkx.linalg.algebraicconnectivity.fiedler_vector"]], "spectral_ordering() (in module networkx.linalg.algebraicconnectivity)": [[1277, "networkx.linalg.algebraicconnectivity.spectral_ordering"]], "attr_matrix() (in module networkx.linalg.attrmatrix)": [[1278, "networkx.linalg.attrmatrix.attr_matrix"]], "attr_sparse_matrix() (in module networkx.linalg.attrmatrix)": [[1279, "networkx.linalg.attrmatrix.attr_sparse_matrix"]], "bethe_hessian_matrix() (in module networkx.linalg.bethehessianmatrix)": [[1280, "networkx.linalg.bethehessianmatrix.bethe_hessian_matrix"]], "adjacency_matrix() (in module networkx.linalg.graphmatrix)": [[1281, "networkx.linalg.graphmatrix.adjacency_matrix"]], "incidence_matrix() (in module networkx.linalg.graphmatrix)": [[1282, "networkx.linalg.graphmatrix.incidence_matrix"]], "directed_combinatorial_laplacian_matrix() (in module networkx.linalg.laplacianmatrix)": [[1283, "networkx.linalg.laplacianmatrix.directed_combinatorial_laplacian_matrix"]], "directed_laplacian_matrix() (in module networkx.linalg.laplacianmatrix)": [[1284, "networkx.linalg.laplacianmatrix.directed_laplacian_matrix"]], "laplacian_matrix() (in module networkx.linalg.laplacianmatrix)": [[1285, "networkx.linalg.laplacianmatrix.laplacian_matrix"]], "normalized_laplacian_matrix() (in module networkx.linalg.laplacianmatrix)": [[1286, "networkx.linalg.laplacianmatrix.normalized_laplacian_matrix"]], "directed_modularity_matrix() (in module networkx.linalg.modularitymatrix)": [[1287, "networkx.linalg.modularitymatrix.directed_modularity_matrix"]], "modularity_matrix() (in module networkx.linalg.modularitymatrix)": [[1288, "networkx.linalg.modularitymatrix.modularity_matrix"]], "adjacency_spectrum() (in module networkx.linalg.spectrum)": [[1289, "networkx.linalg.spectrum.adjacency_spectrum"]], "bethe_hessian_spectrum() (in module networkx.linalg.spectrum)": [[1290, "networkx.linalg.spectrum.bethe_hessian_spectrum"]], "laplacian_spectrum() (in module networkx.linalg.spectrum)": [[1291, "networkx.linalg.spectrum.laplacian_spectrum"]], "modularity_spectrum() (in module networkx.linalg.spectrum)": [[1292, "networkx.linalg.spectrum.modularity_spectrum"]], "normalized_laplacian_spectrum() (in module networkx.linalg.spectrum)": [[1293, "networkx.linalg.spectrum.normalized_laplacian_spectrum"]], "convert_node_labels_to_integers() (in module networkx.relabel)": [[1294, "networkx.relabel.convert_node_labels_to_integers"]], "relabel_nodes() (in module networkx.relabel)": [[1295, "networkx.relabel.relabel_nodes"]], "__init__() (argmap method)": [[1296, "networkx.utils.decorators.argmap.__init__"]], "argmap (class in networkx.utils.decorators)": [[1296, "networkx.utils.decorators.argmap"]], "nodes_or_number() (in module networkx.utils.decorators)": [[1297, "networkx.utils.decorators.nodes_or_number"]], "not_implemented_for() (in module networkx.utils.decorators)": [[1298, "networkx.utils.decorators.not_implemented_for"]], "np_random_state() (in module networkx.utils.decorators)": [[1299, "networkx.utils.decorators.np_random_state"]], "open_file() (in module networkx.utils.decorators)": [[1300, "networkx.utils.decorators.open_file"]], "py_random_state() (in module networkx.utils.decorators)": [[1301, "networkx.utils.decorators.py_random_state"]], "mappedqueue (class in networkx.utils.mapped_queue)": [[1302, "networkx.utils.mapped_queue.MappedQueue"]], "__init__() (mappedqueue method)": [[1302, "networkx.utils.mapped_queue.MappedQueue.__init__"]], "arbitrary_element() (in module networkx.utils.misc)": [[1303, "networkx.utils.misc.arbitrary_element"]], "create_py_random_state() (in module networkx.utils.misc)": [[1304, "networkx.utils.misc.create_py_random_state"]], "create_random_state() (in module networkx.utils.misc)": [[1305, "networkx.utils.misc.create_random_state"]], "dict_to_numpy_array() (in module networkx.utils.misc)": [[1306, "networkx.utils.misc.dict_to_numpy_array"]], "edges_equal() (in module networkx.utils.misc)": [[1307, "networkx.utils.misc.edges_equal"]], "flatten() (in module networkx.utils.misc)": [[1308, "networkx.utils.misc.flatten"]], "graphs_equal() (in module networkx.utils.misc)": [[1309, "networkx.utils.misc.graphs_equal"]], "groups() (in module networkx.utils.misc)": [[1310, "networkx.utils.misc.groups"]], "make_list_of_ints() (in module networkx.utils.misc)": [[1311, "networkx.utils.misc.make_list_of_ints"]], "nodes_equal() (in module networkx.utils.misc)": [[1312, "networkx.utils.misc.nodes_equal"]], "pairwise() (in module networkx.utils.misc)": [[1313, "networkx.utils.misc.pairwise"]], "cumulative_distribution() (in module networkx.utils.random_sequence)": [[1314, "networkx.utils.random_sequence.cumulative_distribution"]], "discrete_sequence() (in module networkx.utils.random_sequence)": [[1315, "networkx.utils.random_sequence.discrete_sequence"]], "powerlaw_sequence() (in module networkx.utils.random_sequence)": [[1316, "networkx.utils.random_sequence.powerlaw_sequence"]], "random_weighted_sample() (in module networkx.utils.random_sequence)": [[1317, "networkx.utils.random_sequence.random_weighted_sample"]], "weighted_choice() (in module networkx.utils.random_sequence)": [[1318, "networkx.utils.random_sequence.weighted_choice"]], "zipf_rv() (in module networkx.utils.random_sequence)": [[1319, "networkx.utils.random_sequence.zipf_rv"]], "cuthill_mckee_ordering() (in module networkx.utils.rcm)": [[1320, "networkx.utils.rcm.cuthill_mckee_ordering"]], "reverse_cuthill_mckee_ordering() (in module networkx.utils.rcm)": [[1321, "networkx.utils.rcm.reverse_cuthill_mckee_ordering"]], "union() (unionfind method)": [[1322, "networkx.utils.union_find.UnionFind.union"]], "networkx.generators.atlas": [[1323, "module-networkx.generators.atlas"]], "networkx.generators.classic": [[1323, "module-networkx.generators.classic"]], "networkx.generators.cographs": [[1323, "module-networkx.generators.cographs"]], "networkx.generators.community": [[1323, "module-networkx.generators.community"]], "networkx.generators.degree_seq": [[1323, "module-networkx.generators.degree_seq"]], "networkx.generators.directed": [[1323, "module-networkx.generators.directed"]], "networkx.generators.duplication": [[1323, "module-networkx.generators.duplication"]], "networkx.generators.ego": [[1323, "module-networkx.generators.ego"]], "networkx.generators.expanders": [[1323, "module-networkx.generators.expanders"]], "networkx.generators.geometric": [[1323, "module-networkx.generators.geometric"]], "networkx.generators.harary_graph": [[1323, 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"PlanarEmbedding.connect_components", "PlanarEmbedding.copy", "PlanarEmbedding.degree", "PlanarEmbedding.edge_subgraph", "PlanarEmbedding.edges", "PlanarEmbedding.get_data", "PlanarEmbedding.get_edge_data", "PlanarEmbedding.has_edge", "PlanarEmbedding.has_node", "PlanarEmbedding.has_predecessor", "PlanarEmbedding.has_successor", "PlanarEmbedding.in_degree", "PlanarEmbedding.in_edges", "PlanarEmbedding.is_directed", "PlanarEmbedding.is_multigraph", "PlanarEmbedding.name", "PlanarEmbedding.nbunch_iter", "PlanarEmbedding.neighbors", "PlanarEmbedding.neighbors_cw_order", "PlanarEmbedding.next_face_half_edge", "PlanarEmbedding.nodes", "PlanarEmbedding.number_of_edges", "PlanarEmbedding.number_of_nodes", "PlanarEmbedding.order", "PlanarEmbedding.out_degree", "PlanarEmbedding.out_edges", "PlanarEmbedding.pred", "PlanarEmbedding.predecessors", "PlanarEmbedding.remove_edge", "PlanarEmbedding.remove_edges_from", "PlanarEmbedding.remove_node", "PlanarEmbedding.remove_nodes_from", 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1036, 1085, 1086, 1088, 1112, 1117, 1140, 1150, 1168, 1175, 1185, 1196, 1197, 1198, 1215, 1235, 1295, 1307, 1309, 1312, 1326, 1336, 1393, 1405, 1412, 1414, 1426], "posbm": 7, "xy": [7, 245], "212": 7, "380": [7, 17, 50, 51], "plot_blockmodel": [7, 17], "convert": [8, 34, 50, 52, 54, 55, 56, 57, 58, 74, 75, 99, 102, 112, 169, 266, 267, 293, 375, 464, 565, 566, 615, 676, 679, 850, 895, 931, 934, 976, 979, 1038, 1085, 1097, 1098, 1099, 1166, 1167, 1273, 1281, 1296, 1297, 1299, 1301, 1306, 1310, 1325, 1332, 1333, 1336, 1337, 1338, 1342, 1345, 1346, 1347, 1348, 1349, 1350, 1353, 1356, 1357, 1361, 1362, 1363, 1364, 1370, 1371, 1376, 1379, 1403, 1404, 1406, 1409, 1411, 1412, 1413, 1416, 1421, 1426], "formula": [8, 299, 316, 322, 380, 385, 618, 688, 1421], "can": [8, 15, 24, 34, 38, 40, 43, 52, 54, 55, 56, 57, 58, 67, 69, 70, 71, 75, 76, 84, 88, 91, 92, 93, 94, 95, 96, 99, 100, 101, 102, 103, 105, 107, 110, 111, 112, 115, 125, 132, 141, 142, 143, 144, 151, 152, 156, 157, 158, 165, 168, 171, 176, 180, 184, 185, 189, 190, 193, 199, 200, 207, 220, 222, 224, 227, 229, 230, 231, 238, 239, 240, 243, 251, 260, 261, 262, 264, 278, 281, 282, 297, 298, 301, 302, 305, 306, 307, 308, 309, 315, 316, 324, 325, 329, 330, 332, 333, 337, 339, 340, 342, 344, 345, 346, 347, 353, 354, 357, 358, 361, 362, 374, 376, 380, 382, 383, 385, 387, 388, 389, 390, 394, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 422, 423, 427, 439, 440, 449, 454, 456, 458, 460, 461, 464, 465, 466, 471, 472, 473, 474, 475, 491, 492, 494, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 530, 540, 553, 575, 577, 581, 586, 588, 597, 598, 601, 602, 604, 615, 616, 617, 619, 626, 628, 629, 630, 633, 641, 643, 647, 652, 653, 654, 655, 657, 658, 660, 661, 662, 667, 668, 669, 676, 677, 678, 679, 680, 687, 688, 689, 690, 691, 720, 722, 723, 724, 725, 726, 729, 730, 731, 748, 749, 751, 762, 767, 770, 775, 786, 791, 796, 850, 853, 854, 855, 856, 857, 862, 865, 867, 870, 871, 873, 874, 878, 879, 882, 887, 888, 892, 895, 898, 899, 900, 901, 902, 907, 910, 912, 914, 916, 917, 921, 925, 928, 931, 934, 935, 936, 937, 938, 943, 946, 947, 948, 951, 952, 955, 956, 960, 963, 968, 973, 976, 979, 980, 981, 982, 983, 988, 991, 992, 993, 995, 998, 999, 1003, 1007, 1010, 1036, 1037, 1038, 1039, 1040, 1042, 1045, 1047, 1059, 1060, 1061, 1063, 1066, 1068, 1082, 1085, 1088, 1102, 1103, 1105, 1129, 1133, 1135, 1137, 1148, 1151, 1154, 1164, 1165, 1166, 1167, 1174, 1175, 1177, 1193, 1196, 1197, 1198, 1206, 1207, 1217, 1218, 1219, 1222, 1235, 1246, 1248, 1250, 1258, 1263, 1264, 1269, 1272, 1275, 1276, 1278, 1279, 1281, 1282, 1283, 1284, 1295, 1296, 1297, 1299, 1301, 1302, 1303, 1320, 1321, 1323, 1324, 1326, 1328, 1329, 1330, 1333, 1334, 1347, 1349, 1352, 1354, 1356, 1357, 1362, 1363, 1371, 1372, 1378, 1380, 1382, 1385, 1387, 1388, 1392, 1393, 1394, 1395, 1396, 1399, 1402, 1404, 1405, 1406, 1408, 1409, 1412, 1425, 1426], "more": [8, 43, 53, 67, 86, 92, 93, 94, 97, 99, 100, 101, 102, 103, 107, 109, 110, 111, 114, 115, 121, 127, 128, 143, 165, 172, 198, 199, 202, 204, 215, 216, 218, 219, 220, 221, 230, 231, 235, 256, 267, 277, 278, 281, 289, 299, 310, 314, 324, 325, 335, 338, 361, 378, 383, 385, 387, 389, 390, 392, 399, 405, 406, 407, 422, 427, 428, 432, 433, 437, 460, 464, 480, 520, 521, 559, 560, 581, 582, 583, 590, 593, 614, 619, 626, 631, 635, 653, 656, 660, 661, 662, 676, 679, 681, 683, 691, 698, 699, 703, 711, 717, 718, 735, 737, 748, 760, 782, 786, 796, 862, 868, 886, 887, 890, 891, 907, 913, 924, 925, 926, 927, 943, 949, 967, 968, 971, 972, 988, 994, 1006, 1007, 1008, 1009, 1037, 1039, 1040, 1042, 1043, 1071, 1094, 1100, 1116, 1119, 1120, 1123, 1130, 1131, 1132, 1133, 1135, 1137, 1138, 1139, 1140, 1141, 1142, 1143, 1185, 1192, 1193, 1206, 1214, 1217, 1218, 1219, 1272, 1287, 1288, 1295, 1296, 1297, 1323, 1326, 1328, 1337, 1345, 1348, 1349, 1350, 1390, 1394, 1395, 1397, 1398, 1399, 1401, 1404, 1405, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426], "express": [8, 92, 110, 184, 315, 329, 330, 383, 384, 618, 619, 873, 916, 955, 998, 1199, 1287, 1326], "than": [8, 11, 34, 43, 55, 97, 99, 101, 102, 103, 115, 128, 142, 143, 144, 161, 199, 214, 215, 216, 218, 219, 221, 227, 231, 235, 241, 256, 277, 278, 281, 288, 289, 297, 298, 299, 304, 306, 307, 310, 311, 315, 316, 321, 324, 325, 326, 328, 329, 330, 341, 352, 358, 361, 374, 380, 381, 383, 384, 385, 387, 389, 390, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 425, 426, 429, 435, 464, 468, 469, 500, 527, 537, 559, 560, 581, 582, 583, 590, 625, 626, 635, 636, 652, 653, 656, 658, 659, 673, 676, 678, 679, 686, 690, 692, 693, 694, 698, 699, 711, 731, 735, 737, 748, 752, 761, 786, 887, 925, 947, 968, 992, 1007, 1038, 1042, 1043, 1060, 1102, 1135, 1154, 1162, 1165, 1167, 1172, 1174, 1185, 1187, 1194, 1198, 1226, 1230, 1231, 1236, 1237, 1238, 1239, 1275, 1276, 1296, 1297, 1326, 1328, 1345, 1348, 1349, 1350, 1353, 1354, 1358, 1365, 1366, 1379, 1382, 1395, 1402, 1404, 1405, 1408, 1413, 1423, 1425], "worst": [8, 210, 211, 212, 221, 228, 235, 264, 293, 294, 338, 345, 346, 347, 440, 513, 515, 516, 517, 518], "reus": [8, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 1131, 1132, 1138, 1139, 1140, 1141, 1142, 1328, 1402], "subcircuit": 8, "multipl": [8, 11, 25, 40, 45, 77, 93, 94, 99, 103, 107, 109, 143, 157, 158, 166, 175, 188, 195, 207, 287, 311, 357, 385, 386, 423, 443, 447, 458, 460, 464, 485, 486, 487, 594, 595, 597, 615, 616, 641, 643, 678, 690, 691, 697, 705, 738, 762, 786, 796, 856, 857, 863, 869, 877, 884, 892, 901, 902, 908, 923, 928, 937, 938, 944, 946, 950, 959, 960, 962, 963, 965, 973, 982, 983, 989, 991, 1002, 1003, 1005, 1010, 1037, 1039, 1040, 1045, 1046, 1102, 1103, 1105, 1127, 1135, 1137, 1216, 1217, 1219, 1285, 1291, 1296, 1298, 1326, 1352, 1378, 1393, 1405, 1406, 1412, 1413, 1417, 1425, 1426], "wherea": [8, 103, 682, 762, 786, 791, 1165, 1417], "cannot": [8, 101, 103, 127, 132, 199, 232, 300, 362, 394, 476, 581, 582, 583, 584, 632, 722, 887, 925, 934, 968, 979, 1007, 1043, 1165, 1208, 1209, 1296, 1298, 1302, 1303, 1326, 1345, 1347, 1348, 1349, 1350], "subformula": 8, "onc": [8, 38, 54, 55, 88, 93, 94, 99, 100, 112, 127, 199, 227, 230, 231, 232, 246, 247, 360, 374, 380, 388, 422, 423, 428, 488, 491, 492, 581, 582, 583, 652, 678, 679, 717, 718, 887, 925, 968, 1007, 1046, 1066, 1087, 1217, 1311, 1326, 1403, 1407], "thu": [8, 88, 101, 103, 115, 215, 216, 220, 256, 258, 331, 418, 419, 427, 428, 462, 477, 500, 512, 583, 679, 698, 699, 760, 762, 796, 1037, 1039, 1040, 1043, 1087, 1112, 1148, 1215, 1217, 1234, 1278, 1279, 1296, 1328, 1402, 1405, 1407], "wai": [8, 27, 52, 53, 55, 75, 86, 88, 93, 97, 99, 100, 101, 102, 103, 104, 107, 110, 115, 132, 152, 157, 158, 165, 184, 226, 281, 297, 298, 315, 330, 337, 356, 588, 598, 615, 618, 678, 691, 730, 760, 791, 796, 854, 856, 857, 862, 873, 899, 901, 902, 907, 915, 916, 935, 937, 938, 943, 955, 980, 982, 983, 988, 996, 998, 1037, 1039, 1040, 1041, 1097, 1165, 1213, 1215, 1217, 1239, 1262, 1269, 1272, 1326, 1328, 1330, 1393, 1394, 1404, 1406, 1411, 1426], "infeas": [8, 422], "circuit_to_formula": 8, "dag_to_branch": [8, 758, 1408], "transfer": [8, 202, 204, 230, 231, 469, 890, 891, 926, 927, 971, 972, 1008, 1009, 1420], "oper": [8, 30, 52, 95, 101, 112, 115, 168, 184, 189, 227, 374, 423, 460, 546, 547, 548, 552, 553, 554, 577, 595, 598, 601, 671, 672, 673, 674, 679, 680, 758, 786, 865, 873, 878, 910, 916, 946, 955, 960, 991, 998, 1036, 1068, 1088, 1103, 1164, 1218, 1219, 1295, 1302, 1319, 1323, 1325, 1326, 1393, 1394, 1400, 1404, 1405, 1406, 1407, 1408, 1411, 1412, 1413, 1414, 1417], "variabl": [8, 94, 132, 373, 530, 540, 618, 619, 732, 796, 1037, 1038, 1039, 1040, 1154, 1165, 1326, 1408, 1412, 1413, 1414, 1420], "formula_to_str": 8, "_to_str": 8, "root": [8, 67, 84, 293, 294, 338, 387, 389, 390, 394, 449, 460, 559, 577, 609, 671, 673, 678, 704, 728, 730, 739, 760, 791, 1119, 1120, 1125, 1126, 1145, 1147, 1235, 1271, 1272, 1323, 1365, 1366, 1393, 1406, 1407, 1408, 1412, 1413, 1423, 1425], "children": [8, 460, 577, 1145, 1155, 1272, 1365, 1366], "otherwis": [8, 92, 110, 146, 149, 171, 178, 184, 185, 198, 217, 230, 249, 250, 284, 297, 298, 303, 306, 307, 311, 315, 316, 322, 323, 324, 325, 326, 329, 330, 343, 353, 358, 393, 394, 395, 396, 397, 398, 410, 411, 412, 418, 419, 422, 425, 426, 462, 463, 464, 470, 479, 488, 490, 494, 495, 496, 498, 499, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 521, 555, 562, 563, 568, 572, 574, 584, 586, 588, 597, 601, 616, 618, 619, 633, 663, 673, 687, 688, 689, 696, 698, 699, 734, 735, 736, 737, 751, 848, 867, 873, 874, 886, 893, 912, 916, 917, 924, 929, 934, 948, 955, 956, 967, 974, 979, 993, 998, 999, 1006, 1068, 1091, 1135, 1137, 1165, 1185, 1197, 1217, 1270, 1282, 1283, 1284, 1307, 1309, 1312, 1342, 1356, 1357, 1376, 1409, 1413, 1426], "child": [8, 1147, 1272], "must": [8, 11, 93, 94, 95, 99, 100, 103, 110, 151, 152, 158, 161, 171, 204, 206, 207, 214, 215, 216, 219, 230, 231, 232, 252, 253, 257, 258, 259, 260, 261, 262, 264, 267, 268, 269, 271, 273, 276, 281, 285, 297, 298, 306, 307, 315, 316, 317, 318, 319, 324, 325, 327, 329, 330, 342, 361, 362, 363, 378, 382, 385, 391, 410, 411, 412, 413, 425, 429, 440, 471, 472, 473, 474, 475, 545, 546, 547, 548, 549, 550, 551, 553, 555, 556, 557, 558, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 577, 578, 579, 580, 584, 585, 586, 587, 588, 589, 593, 597, 599, 601, 602, 603, 604, 615, 626, 627, 632, 633, 635, 636, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 671, 672, 673, 674, 680, 690, 692, 698, 699, 707, 721, 734, 735, 736, 737, 789, 796, 853, 854, 857, 867, 891, 892, 898, 899, 902, 912, 928, 934, 938, 972, 973, 979, 983, 1010, 1037, 1038, 1039, 1040, 1063, 1071, 1085, 1102, 1133, 1137, 1162, 1165, 1173, 1176, 1186, 1188, 1190, 1193, 1197, 1199, 1209, 1213, 1217, 1219, 1235, 1239, 1240, 1270, 1275, 1276, 1277, 1278, 1279, 1295, 1296, 1298, 1307, 1309, 1310, 1311, 1312, 1315, 1333, 1337, 1338, 1339, 1340, 1359, 1361, 1362, 1363, 1364, 1365, 1366, 1376, 1393, 1394, 1395, 1407, 1426], "NOT": [8, 110, 199, 549, 550, 551, 748, 887, 925, 968, 1007], "util": [8, 14, 36, 44, 45, 93, 97, 102, 103, 229, 230, 231, 316, 374, 423, 425, 426, 429, 460, 496, 678, 679, 758, 1044, 1242, 1299, 1301, 1303, 1310, 1319, 1320, 1321, 1325, 1402, 1406, 1407, 1411, 1413, 1416, 1419], "arbitrary_el": [8, 1392, 1413], "nb": [8, 1331, 1334], "left": [8, 71, 115, 183, 311, 312, 322, 324, 325, 385, 559, 560, 584, 616, 688, 689, 739, 1106, 1134, 1136, 1146, 1179, 1206, 1280, 1355, 1358, 1404], "right": [8, 71, 110, 111, 115, 152, 206, 322, 385, 427, 428, 500, 559, 560, 584, 585, 587, 588, 615, 616, 688, 689, 739, 854, 935, 980, 1134, 1136, 1146, 1155, 1157, 1179, 1206, 1213, 1215, 1270, 1280], "littl": [8, 94, 298, 307], "mislead": 8, "That": [8, 97, 132, 165, 212, 221, 227, 295, 385, 436, 465, 525, 535, 555, 588, 657, 671, 672, 673, 674, 691, 704, 717, 791, 862, 907, 943, 988, 1046, 1162, 1210, 1296, 1388, 1404, 1409], "okai": 8, "becaus": [8, 11, 54, 69, 94, 99, 101, 102, 103, 112, 132, 161, 215, 216, 220, 255, 311, 378, 387, 389, 390, 394, 411, 412, 427, 494, 498, 499, 500, 510, 569, 585, 587, 615, 616, 632, 652, 934, 979, 1038, 1236, 1273, 1296, 1303, 1326, 1345, 1350, 1404, 1407, 1416], "AND": [8, 110, 598, 748, 762], "OR": [8, 110, 157, 175, 188, 856, 869, 877, 901, 937, 947, 950, 959, 982, 992], "symmetr": [8, 145, 148, 237, 545, 586, 593, 761, 1173, 1192, 1235, 1246, 1250, 1251, 1256, 1258, 1269, 1320, 1321, 1387], "It": [8, 52, 56, 58, 92, 93, 94, 97, 99, 101, 102, 104, 107, 110, 112, 115, 132, 172, 184, 207, 214, 215, 216, 229, 230, 231, 249, 260, 261, 262, 264, 278, 310, 316, 324, 325, 326, 343, 346, 347, 351, 353, 412, 414, 415, 416, 417, 418, 419, 429, 438, 440, 452, 457, 464, 480, 496, 500, 508, 530, 540, 545, 559, 560, 565, 566, 567, 582, 588, 594, 595, 598, 600, 601, 615, 619, 628, 629, 630, 652, 658, 659, 663, 671, 674, 692, 717, 718, 719, 760, 761, 762, 791, 796, 868, 873, 892, 913, 916, 928, 949, 955, 973, 994, 998, 1010, 1012, 1013, 1018, 1037, 1038, 1039, 1040, 1054, 1117, 1170, 1174, 1200, 1201, 1206, 1207, 1210, 1217, 1223, 1227, 1234, 1243, 1244, 1245, 1246, 1247, 1248, 1249, 1250, 1251, 1253, 1254, 1258, 1261, 1263, 1264, 1269, 1275, 1276, 1277, 1280, 1296, 1297, 1323, 1324, 1326, 1328, 1343, 1382, 1383, 1393, 1395, 1398, 1402, 1404, 1407, 1408, 1409, 1411, 1412, 1413, 1426], "just": [8, 99, 102, 103, 104, 184, 199, 338, 374, 439, 464, 559, 560, 577, 660, 661, 662, 692, 791, 873, 887, 916, 925, 946, 955, 960, 968, 991, 998, 1007, 1120, 1126, 1229, 1278, 1279, 1296, 1328, 1393, 1404, 1406], "operand": 8, "predict": [8, 567, 568, 569, 570, 571, 572, 573, 574, 591, 592, 758, 1325, 1402, 1406, 1412], "henc": [8, 168, 189, 521, 865, 878, 910, 946, 960, 991, 1059, 1202, 1383], "doe": [8, 77, 93, 94, 99, 101, 102, 103, 104, 114, 115, 132, 147, 153, 154, 165, 168, 189, 207, 208, 227, 228, 229, 230, 231, 232, 293, 308, 339, 340, 342, 343, 352, 357, 373, 382, 385, 410, 414, 426, 450, 469, 494, 495, 496, 497, 498, 499, 500, 502, 503, 506, 507, 509, 510, 511, 512, 534, 544, 549, 550, 551, 564, 566, 583, 584, 586, 589, 601, 612, 626, 627, 678, 691, 693, 694, 698, 699, 717, 718, 721, 722, 723, 724, 725, 726, 762, 862, 865, 878, 892, 907, 910, 928, 943, 946, 960, 973, 988, 991, 1010, 1038, 1043, 1066, 1070, 1072, 1081, 1102, 1103, 1105, 1106, 1107, 1109, 1114, 1173, 1175, 1177, 1192, 1207, 1222, 1223, 1227, 1229, 1234, 1241, 1296, 1300, 1303, 1326, 1333, 1334, 1341, 1342, 1344, 1351, 1353, 1354, 1355, 1356, 1357, 1358, 1371, 1379, 1380, 1381, 1383, 1393, 1404, 1405, 1406, 1410, 1417, 1426], "necessarili": [8, 99, 341, 451, 483, 559, 560, 641, 643, 1038, 1219], "behav": [8, 88, 103, 159, 190, 200, 220, 351, 858, 879, 888, 903, 939, 969, 984, 1229, 1296, 1395, 1404], "everi": [8, 11, 57, 88, 93, 109, 112, 120, 144, 157, 161, 177, 211, 212, 220, 221, 229, 230, 231, 235, 243, 264, 287, 295, 300, 324, 325, 343, 352, 380, 397, 437, 439, 440, 450, 462, 471, 472, 473, 474, 475, 477, 483, 484, 491, 512, 516, 565, 606, 614, 615, 619, 632, 633, 635, 636, 663, 685, 687, 688, 717, 718, 791, 856, 901, 937, 982, 1052, 1053, 1054, 1070, 1071, 1072, 1085, 1086, 1102, 1103, 1105, 1106, 1107, 1108, 1109, 1110, 1111, 1114, 1115, 1116, 1117, 1148, 1162, 1195, 1216, 1217, 1257, 1264, 1278, 1279, 1296, 1407], "left_subformula": 8, "right_subformula": 8, "in_degre": [8, 166, 188, 491, 678, 863, 877, 944, 959, 1177, 1207, 1208, 1404, 1406, 1407, 1426], "ha": [8, 11, 16, 44, 67, 88, 91, 93, 94, 95, 97, 99, 100, 101, 102, 103, 105, 107, 110, 112, 116, 120, 127, 152, 161, 165, 166, 173, 174, 175, 184, 188, 198, 207, 212, 214, 215, 219, 220, 226, 227, 229, 230, 231, 232, 235, 238, 239, 240, 241, 242, 243, 244, 247, 249, 252, 269, 271, 272, 273, 274, 275, 276, 282, 289, 291, 293, 294, 295, 300, 305, 310, 324, 331, 343, 352, 355, 356, 363, 364, 365, 373, 378, 380, 381, 383, 384, 385, 386, 391, 393, 394, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 424, 427, 428, 429, 439, 450, 458, 460, 466, 467, 468, 471, 472, 473, 474, 475, 476, 477, 480, 491, 492, 493, 494, 495, 496, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 522, 564, 566, 577, 578, 581, 590, 593, 605, 607, 610, 611, 622, 623, 624, 628, 629, 630, 632, 633, 634, 635, 636, 638, 646, 647, 649, 652, 657, 658, 682, 688, 690, 692, 697, 711, 717, 718, 729, 730, 731, 739, 749, 786, 791, 854, 862, 863, 869, 873, 877, 886, 892, 899, 907, 908, 916, 924, 928, 935, 943, 944, 948, 950, 955, 959, 967, 973, 980, 988, 989, 993, 998, 1006, 1010, 1040, 1043, 1045, 1066, 1068, 1070, 1072, 1075, 1080, 1084, 1098, 1099, 1101, 1102, 1103, 1105, 1122, 1130, 1145, 1154, 1160, 1162, 1165, 1176, 1180, 1185, 1193, 1195, 1196, 1197, 1198, 1199, 1207, 1210, 1211, 1215, 1217, 1222, 1234, 1239, 1243, 1244, 1248, 1249, 1254, 1259, 1261, 1264, 1267, 1269, 1270, 1272, 1275, 1276, 1277, 1278, 1279, 1281, 1282, 1283, 1284, 1285, 1286, 1289, 1291, 1293, 1296, 1300, 1326, 1328, 1330, 1333, 1334, 1353, 1354, 1371, 1372, 1379, 1382, 1393, 1394, 1395, 1398, 1403, 1404, 1405, 1406, 1407, 1409, 1413, 1414, 1416, 1423, 1425], "output": [8, 13, 16, 89, 93, 101, 102, 103, 109, 197, 287, 288, 345, 374, 380, 494, 498, 499, 509, 510, 575, 588, 677, 678, 691, 722, 1045, 1193, 1197, 1199, 1269, 1296, 1326, 1334, 1341, 1344, 1355, 1358, 1399, 1402, 1404, 1406, 1411, 1413, 1414, 1426], "two": [8, 11, 16, 27, 34, 38, 43, 54, 55, 57, 58, 65, 67, 71, 88, 93, 95, 99, 100, 102, 109, 112, 114, 115, 120, 132, 151, 171, 175, 184, 185, 188, 202, 207, 211, 212, 213, 214, 215, 216, 217, 220, 221, 226, 227, 230, 231, 232, 245, 249, 251, 252, 253, 257, 258, 260, 261, 262, 265, 269, 270, 271, 272, 273, 274, 275, 276, 282, 285, 286, 287, 289, 305, 311, 315, 316, 322, 326, 329, 330, 337, 341, 343, 345, 351, 352, 358, 359, 377, 380, 381, 383, 391, 411, 412, 419, 423, 428, 429, 430, 431, 442, 443, 444, 445, 447, 452, 453, 454, 457, 462, 471, 472, 473, 474, 475, 476, 480, 491, 494, 498, 499, 500, 502, 503, 506, 508, 509, 510, 511, 521, 545, 549, 550, 551, 555, 559, 560, 561, 562, 563, 564, 565, 566, 568, 569, 572, 574, 578, 584, 585, 586, 587, 588, 593, 598, 605, 607, 608, 610, 611, 615, 619, 626, 627, 629, 632, 633, 635, 636, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 671, 672, 673, 674, 675, 676, 680, 692, 694, 731, 732, 738, 739, 760, 761, 762, 780, 786, 791, 796, 853, 867, 869, 873, 874, 877, 890, 892, 898, 912, 916, 917, 926, 928, 934, 946, 948, 950, 955, 956, 959, 960, 971, 973, 979, 991, 993, 998, 999, 1008, 1010, 1019, 1020, 1021, 1022, 1036, 1037, 1039, 1040, 1056, 1084, 1088, 1098, 1100, 1101, 1106, 1107, 1108, 1109, 1114, 1116, 1134, 1146, 1147, 1149, 1151, 1152, 1156, 1174, 1185, 1186, 1193, 1194, 1195, 1196, 1197, 1198, 1199, 1204, 1207, 1210, 1211, 1215, 1217, 1218, 1243, 1244, 1253, 1271, 1272, 1275, 1276, 1294, 1295, 1296, 1323, 1324, 1326, 1328, 1359, 1360, 1363, 1393, 1394, 1395, 1397, 1402, 1404, 1405, 1406, 1407, 1410, 1411, 1413, 1425], "layer": [8, 36, 55, 61, 67, 103, 438, 705, 1038, 1109, 1420], "third": [8, 102, 114, 249, 422, 467, 585, 587, 734, 736, 1217, 1226, 1262, 1263, 1326, 1407], "appear": [8, 83, 93, 95, 99, 100, 102, 179, 204, 230, 231, 238, 243, 246, 247, 277, 363, 364, 365, 378, 451, 452, 453, 455, 466, 470, 584, 585, 587, 588, 675, 679, 707, 730, 734, 736, 891, 972, 1036, 1088, 1102, 1136, 1150, 1152, 1154, 1157, 1159, 1187, 1188, 1277, 1282, 1323, 1324, 1345, 1348, 1349, 1350, 1382, 1407, 1413, 1414], "both": [8, 52, 55, 92, 93, 94, 100, 101, 102, 103, 115, 161, 164, 204, 214, 215, 216, 217, 240, 257, 258, 259, 264, 282, 286, 287, 289, 337, 358, 379, 383, 415, 417, 418, 419, 423, 427, 440, 470, 502, 506, 545, 575, 581, 598, 600, 601, 602, 603, 604, 605, 606, 607, 610, 611, 615, 621, 635, 636, 653, 654, 655, 676, 711, 720, 760, 761, 762, 782, 891, 972, 1020, 1036, 1066, 1075, 1080, 1084, 1088, 1097, 1120, 1126, 1144, 1165, 1189, 1192, 1199, 1207, 1210, 1211, 1213, 1215, 1282, 1296, 1326, 1328, 1358, 1363, 1364, 1387, 1393, 1395, 1402, 1413, 1416, 1417, 1425, 1426], "negat": 8, "sole": [8, 786, 1278, 1279, 1326], "fourth": [8, 230, 231, 1326, 1404], "digraph": [8, 10, 11, 16, 21, 25, 41, 45, 56, 61, 67, 69, 70, 82, 88, 101, 102, 115, 132, 151, 152, 156, 157, 158, 160, 162, 163, 165, 166, 168, 170, 171, 172, 175, 176, 185, 186, 187, 188, 189, 192, 193, 194, 195, 196, 198, 199, 202, 204, 207, 208, 216, 227, 229, 230, 231, 240, 246, 247, 299, 308, 314, 318, 319, 321, 327, 328, 334, 335, 336, 337, 339, 340, 342, 343, 388, 391, 393, 396, 397, 398, 399, 401, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 430, 431, 437, 450, 452, 453, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 481, 482, 492, 494, 495, 496, 497, 498, 499, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 513, 514, 518, 519, 523, 555, 566, 575, 576, 577, 588, 590, 613, 615, 623, 630, 636, 643, 644, 652, 656, 657, 658, 659, 663, 678, 688, 690, 693, 696, 697, 698, 699, 700, 701, 702, 706, 707, 708, 709, 711, 716, 717, 718, 719, 721, 722, 723, 724, 725, 726, 740, 741, 744, 745, 746, 747, 748, 749, 750, 752, 760, 789, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 904, 905, 906, 907, 908, 911, 912, 913, 915, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 935, 936, 937, 938, 940, 941, 942, 943, 949, 957, 958, 963, 964, 965, 966, 967, 968, 972, 973, 974, 975, 977, 978, 980, 981, 982, 983, 985, 986, 987, 988, 989, 994, 996, 1000, 1001, 1003, 1004, 1005, 1006, 1007, 1010, 1035, 1037, 1038, 1039, 1040, 1041, 1052, 1062, 1066, 1070, 1072, 1075, 1080, 1083, 1084, 1098, 1099, 1101, 1118, 1135, 1150, 1154, 1168, 1169, 1170, 1173, 1177, 1178, 1180, 1182, 1183, 1184, 1185, 1189, 1217, 1270, 1272, 1273, 1274, 1283, 1284, 1287, 1290, 1292, 1298, 1323, 1326, 1333, 1337, 1342, 1356, 1357, 1362, 1365, 1366, 1371, 1393, 1399, 1401, 1402, 1404, 1405, 1406, 1407, 1408, 1409, 1411, 1412, 1413, 1414, 1416, 1417, 1424, 1425, 1426], "add_nod": [8, 11, 26, 34, 69, 74, 89, 102, 157, 184, 246, 339, 340, 398, 422, 491, 492, 496, 504, 505, 508, 522, 523, 605, 607, 610, 611, 691, 796, 856, 873, 901, 916, 937, 955, 982, 998, 1037, 1039, 1040, 1086, 1275, 1326, 1345, 1407, 1408, 1417, 1426], "get_node_attribut": [8, 39, 44, 71, 1213, 1404], "600": [8, 10, 12], "font_siz": [8, 16, 21, 25, 32, 35, 38, 45, 46, 1133, 1134, 1136], "22": [8, 35, 64, 66, 383, 384, 1271, 1323, 1403, 1408, 1412, 1422], "multipartite_layout": [8, 36, 61, 67, 1412, 1414, 1420], "subset_kei": [8, 36, 61, 67, 1109], "equal": [8, 36, 81, 144, 214, 215, 216, 230, 231, 238, 269, 271, 273, 276, 288, 297, 298, 300, 303, 306, 307, 310, 311, 312, 315, 316, 320, 323, 324, 325, 329, 330, 331, 373, 410, 411, 412, 413, 418, 419, 428, 471, 474, 476, 491, 494, 495, 496, 498, 499, 502, 503, 504, 505, 506, 507, 508, 509, 510, 525, 535, 545, 552, 553, 554, 555, 568, 572, 605, 623, 657, 671, 672, 673, 674, 687, 688, 689, 690, 721, 722, 740, 741, 753, 761, 791, 1112, 1116, 1162, 1165, 1198, 1204, 1230, 1239, 1271, 1280, 1291, 1307, 1309, 1312, 1398, 1399], "plot_circuit": [8, 17], "southern": [9, 1265], "women": [9, 1265, 1398, 1406], "unipartit": [9, 115, 258, 259, 358], "properti": [9, 11, 18, 22, 33, 63, 86, 101, 102, 103, 112, 134, 159, 161, 166, 168, 175, 176, 179, 184, 188, 189, 190, 200, 284, 285, 286, 287, 288, 363, 364, 365, 388, 476, 500, 545, 569, 619, 685, 858, 863, 865, 869, 870, 873, 877, 878, 879, 888, 903, 908, 910, 916, 939, 944, 946, 950, 951, 955, 959, 960, 969, 984, 989, 991, 998, 1085, 1086, 1122, 1134, 1136, 1193, 1202, 1217, 1219, 1269, 1283, 1284, 1326, 1328, 1383, 1398, 1405, 1406, 1407, 1408, 1413, 1417, 1426], "These": [9, 52, 58, 73, 79, 86, 93, 94, 105, 112, 336, 385, 494, 512, 559, 671, 673, 732, 748, 779, 786, 1038, 1045, 1047, 1323, 1326, 1385, 1387, 1392, 1394, 1395, 1397, 1399, 1404, 1405, 1411, 1426], "were": [9, 65, 88, 99, 101, 104, 215, 216, 220, 289, 305, 410, 437, 460, 588, 962, 1002, 1199, 1393, 1395, 1399, 1402, 1405, 1406, 1407, 1413, 1416], "et": [9, 210, 226, 227, 315, 316, 322, 330, 334, 337, 345, 352, 358, 373, 380, 381, 423, 425, 426, 451, 569, 591, 592, 681, 682, 684, 693, 1202], "al": [9, 210, 226, 227, 315, 316, 322, 330, 334, 337, 345, 352, 358, 373, 380, 381, 423, 425, 426, 451, 569, 591, 592, 681, 682, 684, 693, 1202, 1407, 1413], "1930": [9, 1396], "thei": [9, 54, 58, 65, 71, 92, 93, 94, 97, 99, 100, 101, 102, 103, 104, 105, 107, 112, 132, 151, 165, 207, 213, 220, 249, 285, 287, 288, 296, 297, 298, 301, 302, 306, 307, 308, 309, 351, 362, 374, 391, 396, 427, 451, 452, 453, 454, 464, 465, 471, 472, 473, 474, 475, 496, 504, 505, 508, 512, 546, 547, 548, 559, 560, 576, 583, 586, 588, 600, 604, 675, 676, 704, 717, 750, 760, 786, 853, 862, 892, 898, 907, 928, 934, 943, 962, 973, 979, 988, 1002, 1010, 1036, 1038, 1066, 1085, 1088, 1109, 1120, 1126, 1133, 1135, 1137, 1151, 1159, 1165, 1193, 1197, 1198, 1217, 1271, 1272, 1323, 1328, 1353, 1354, 1356, 1357, 1359, 1363, 1394, 1396, 1402, 1404, 1406, 1409, 1414, 1426], "repres": [9, 11, 26, 43, 52, 54, 57, 67, 92, 99, 107, 115, 230, 231, 265, 281, 283, 286, 287, 288, 291, 292, 338, 350, 361, 362, 363, 377, 378, 380, 381, 382, 385, 386, 391, 448, 452, 453, 455, 457, 460, 465, 466, 494, 495, 498, 499, 500, 502, 503, 506, 507, 509, 510, 521, 565, 577, 578, 579, 580, 586, 588, 609, 615, 618, 619, 656, 660, 664, 667, 676, 679, 691, 692, 695, 697, 698, 699, 700, 702, 728, 730, 731, 734, 736, 739, 752, 786, 791, 796, 1019, 1020, 1021, 1022, 1037, 1038, 1039, 1040, 1045, 1081, 1102, 1140, 1151, 1185, 1193, 1194, 1196, 1197, 1198, 1199, 1209, 1217, 1240, 1243, 1246, 1250, 1258, 1267, 1269, 1272, 1273, 1278, 1279, 1323, 1324, 1326, 1329, 1330, 1346, 1347, 1388, 1393, 1406], "observ": [9, 13, 132, 223, 1414, 1426], "attend": 9, "14": [9, 11, 16, 19, 25, 38, 44, 56, 59, 64, 66, 71, 229, 230, 231, 383, 384, 405, 406, 501, 619, 690, 1150, 1242, 1250, 1262, 1406, 1408, 1426], "event": [9, 25, 99, 100, 110, 1165, 1229, 1300], "18": [9, 44, 64, 66, 93, 324, 325, 345, 383, 384, 618, 1169, 1249, 1255, 1258, 1260, 1263, 1269, 1393, 1406, 1416, 1417, 1421, 1426], "bipartit": [9, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 350, 351, 358, 377, 439, 440, 443, 581, 588, 758, 1043, 1106, 1151, 1203, 1204, 1205, 1265, 1325, 1395, 1398, 1399, 1400, 1401, 1406, 1407, 1411, 1413, 1417, 1421, 1425], "biadjac": [9, 282, 283, 1400, 1406], "7": [9, 12, 14, 19, 25, 35, 44, 46, 63, 64, 65, 66, 68, 89, 99, 101, 102, 115, 125, 151, 158, 170, 171, 192, 207, 232, 268, 297, 299, 314, 322, 327, 332, 333, 339, 340, 342, 362, 374, 380, 391, 403, 410, 413, 414, 415, 423, 424, 425, 426, 441, 445, 446, 483, 496, 501, 508, 511, 512, 555, 581, 586, 618, 619, 630, 652, 658, 663, 671, 674, 680, 695, 703, 706, 707, 708, 730, 747, 750, 761, 796, 853, 857, 866, 867, 881, 892, 898, 902, 911, 912, 915, 920, 928, 934, 938, 947, 973, 979, 983, 992, 996, 1010, 1037, 1039, 1040, 1052, 1053, 1085, 1100, 1104, 1148, 1212, 1242, 1248, 1250, 1251, 1255, 1258, 1260, 1273, 1323, 1326, 1330, 1339, 1340, 1345, 1348, 1349, 1350, 1382, 1392, 1394, 1402, 1403, 1405, 1408, 1409, 1410, 1411, 1412, 1413, 1426], "12": [9, 11, 19, 25, 44, 50, 55, 58, 64, 65, 66, 89, 91, 93, 229, 230, 231, 265, 345, 380, 381, 392, 399, 405, 406, 407, 449, 486, 501, 516, 568, 572, 574, 606, 616, 1052, 1053, 1054, 1133, 1136, 1150, 1244, 1245, 1249, 1254, 1257, 1263, 1335, 1406, 1408, 1412, 1426], "9": [9, 11, 12, 19, 25, 35, 44, 46, 63, 64, 65, 66, 68, 82, 89, 101, 102, 111, 115, 125, 232, 293, 295, 339, 340, 342, 346, 347, 356, 374, 380, 405, 406, 424, 438, 449, 494, 496, 501, 504, 505, 508, 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92, 93, 94, 100, 112, 315, 317, 318, 319, 330, 391, 483, 484, 586, 691, 1222, 1267, 1403], "evelyn": 9, "jefferson": 9, "laura": 9, "mandevil": 9, "theresa": 9, "anderson": 9, "brenda": 9, "roger": 9, "charlott": 9, "mcdowd": 9, "franc": 9, "eleanor": 9, "nye": 9, "pearl": [9, 132], "oglethorp": 9, "ruth": 9, "desand": 9, "vern": 9, "sanderson": 9, "myra": 9, "liddel": 9, "katherina": 9, "sylvia": 9, "avondal": 9, "nora": 9, "fayett": 9, "helen": 9, "lloyd": 9, "dorothi": 9, "murchison": 9, "olivia": 9, "carleton": 9, "flora": 9, "price": 9, "meet": [9, 94, 1165, 1196, 1197, 1198], "50": [9, 25, 30, 34, 40, 50, 54, 55, 56, 57, 64, 65, 272, 312, 1117, 1193, 1197, 1198, 1251, 1297, 1302], "45": [9, 58, 64, 110, 226, 300, 409, 1175], "57": [9, 64], "46": [9, 64, 235, 564, 619, 1264], "24": [9, 19, 37, 64, 66, 68, 103, 383, 384, 496, 505, 508, 703, 1212, 1229, 1244, 1262, 1271, 1403], "32": [9, 64, 66, 68, 209, 211, 212, 383, 384, 564, 703, 1403, 1411], "36": [9, 21, 64, 68, 752, 1150, 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762, 786, 1036, 1088, 1388, 1413], "add": [10, 11, 26, 34, 41, 45, 49, 52, 61, 71, 88, 89, 91, 93, 94, 101, 102, 105, 106, 115, 151, 152, 153, 154, 156, 157, 158, 164, 207, 222, 223, 229, 282, 285, 341, 374, 411, 412, 423, 428, 430, 431, 450, 460, 581, 582, 583, 589, 614, 615, 618, 619, 654, 690, 701, 717, 718, 796, 850, 853, 854, 855, 856, 857, 892, 895, 898, 899, 900, 901, 902, 928, 931, 934, 935, 936, 937, 938, 973, 976, 979, 980, 981, 982, 983, 984, 1010, 1037, 1038, 1039, 1040, 1042, 1049, 1052, 1053, 1054, 1100, 1154, 1165, 1172, 1185, 1207, 1210, 1217, 1219, 1233, 1234, 1236, 1302, 1326, 1353, 1354, 1356, 1357, 1379, 1380, 1383, 1393, 1394, 1395, 1398, 1404, 1406, 1407, 1408, 1409, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426], "compressor": [10, 690, 786], "do": [10, 55, 75, 88, 92, 93, 94, 96, 99, 101, 102, 106, 107, 109, 111, 115, 133, 165, 184, 199, 202, 204, 230, 231, 238, 243, 277, 278, 280, 362, 380, 410, 411, 412, 418, 419, 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1290, 1295, 1296, 1299, 1301, 1383, 1395, 1407, 1408, 1412, 1413, 1414, 1419, 1426], "follow": [11, 25, 44, 49, 52, 53, 65, 67, 83, 86, 91, 92, 93, 94, 95, 97, 99, 100, 101, 102, 103, 108, 110, 111, 128, 132, 151, 161, 171, 183, 207, 213, 227, 229, 230, 231, 243, 280, 305, 338, 343, 351, 362, 373, 378, 380, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 440, 452, 453, 465, 466, 496, 502, 503, 504, 505, 506, 507, 508, 588, 598, 599, 602, 615, 636, 679, 748, 750, 760, 762, 791, 853, 867, 892, 898, 912, 928, 934, 948, 973, 979, 993, 1010, 1102, 1103, 1105, 1144, 1165, 1175, 1179, 1185, 1188, 1200, 1201, 1209, 1219, 1225, 1233, 1234, 1241, 1251, 1260, 1274, 1275, 1276, 1277, 1281, 1296, 1315, 1323, 1326, 1328, 1329, 1388, 1393, 1395, 1399, 1404, 1406, 1407, 1409, 1411, 1412, 1413, 1425, 1426], "given": [11, 38, 44, 62, 64, 67, 91, 99, 101, 103, 112, 116, 141, 142, 144, 152, 158, 193, 197, 208, 211, 212, 227, 229, 235, 236, 248, 249, 260, 264, 266, 269, 271, 273, 274, 276, 279, 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311, 312, 322, 323, 324, 325, 329, 330, 337, 343, 346, 347, 362, 371, 373, 374, 380, 381, 382, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 423, 425, 426, 427, 428, 430, 431, 449, 464, 466, 494, 498, 499, 500, 509, 510, 545, 555, 564, 566, 569, 574, 581, 614, 616, 660, 667, 680, 688, 691, 704, 706, 707, 708, 710, 711, 712, 713, 714, 715, 717, 732, 738, 758, 761, 762, 786, 791, 796, 887, 925, 934, 935, 968, 979, 980, 1007, 1036, 1037, 1038, 1041, 1043, 1082, 1088, 1182, 1229, 1235, 1253, 1267, 1296, 1320, 1321, 1323, 1326, 1383, 1387, 1392, 1395, 1402, 1403, 1404, 1406, 1407, 1408, 1409, 1411, 1412, 1421, 1425], "obtain": [11, 91, 165, 207, 282, 345, 346, 347, 380, 383, 387, 388, 389, 390, 394, 465, 511, 606, 618, 619, 656, 722, 742, 743, 760, 796, 862, 892, 907, 928, 943, 973, 988, 1010, 1037, 1039, 1040, 1164, 1253, 1272, 1278, 1279, 1323, 1326, 1356, 1357, 1402, 1426], "seri": [11, 444, 616, 680, 1215, 1286], "finit": [11, 462, 494, 495, 498, 499, 502, 503, 506, 507, 509, 510, 514, 518, 1177, 1179, 1192, 1222], "end": [11, 25, 36, 52, 95, 101, 106, 153, 154, 206, 215, 227, 267, 268, 300, 332, 333, 342, 371, 372, 427, 614, 618, 619, 626, 627, 631, 632, 634, 635, 636, 639, 640, 650, 651, 652, 653, 654, 655, 660, 664, 667, 677, 678, 680, 734, 736, 1038, 1061, 1066, 1075, 1080, 1082, 1084, 1117, 1133, 1135, 1152, 1165, 1206, 1229, 1326, 1333, 1334, 1337, 1338, 1339, 1340, 1342, 1344, 1350, 1353, 1357, 1358, 1368, 1371, 1372, 1375, 1376, 1379, 1404, 1413], "In": [11, 16, 27, 43, 54, 57, 58, 88, 92, 93, 94, 95, 97, 99, 100, 101, 103, 110, 115, 127, 132, 133, 175, 184, 199, 217, 229, 230, 231, 235, 240, 257, 258, 259, 278, 283, 286, 288, 289, 299, 311, 312, 324, 325, 329, 350, 357, 378, 379, 380, 410, 413, 414, 415, 422, 429, 443, 447, 450, 458, 460, 494, 498, 499, 501, 510, 565, 568, 572, 574, 590, 591, 615, 619, 621, 652, 653, 654, 657, 658, 663, 670, 675, 676, 690, 691, 701, 703, 717, 718, 719, 730, 732, 740, 741, 742, 743, 761, 762, 767, 770, 789, 791, 796, 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324, 327, 376, 383, 387, 389, 390, 394, 410, 411, 412, 418, 419, 494, 498, 499, 510, 564, 566, 626, 627, 632, 640, 643, 652, 693, 711, 758, 1188, 1207, 1210, 1407], "orbit": 11, "up": [11, 70, 80, 93, 94, 97, 99, 100, 101, 104, 107, 132, 133, 346, 347, 377, 423, 427, 509, 530, 540, 577, 619, 652, 653, 657, 748, 1036, 1038, 1061, 1066, 1082, 1088, 1102, 1144, 1148, 1173, 1213, 1215, 1272, 1326, 1328, 1355, 1358, 1395, 1396, 1402, 1404, 1406, 1410, 1411, 1413, 1414, 1416, 1417, 1420, 1426], "reveal": [11, 711, 786], "maximum": [11, 112, 115, 209, 210, 211, 212, 214, 215, 217, 222, 224, 227, 257, 259, 264, 277, 278, 279, 281, 288, 296, 304, 311, 312, 315, 316, 317, 318, 319, 321, 324, 328, 330, 339, 341, 342, 343, 346, 347, 352, 356, 361, 373, 377, 380, 382, 383, 385, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 428, 440, 472, 473, 494, 498, 499, 500, 501, 502, 503, 506, 507, 509, 510, 520, 521, 564, 566, 581, 583, 589, 591, 592, 670, 671, 672, 673, 674, 676, 691, 693, 694, 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691, 1268], "analysi": [15, 23, 47, 50, 52, 55, 86, 100, 101, 103, 105, 107, 110, 228, 232, 257, 258, 259, 260, 261, 262, 286, 288, 289, 299, 305, 379, 383, 412, 431, 437, 462, 494, 500, 619, 691, 751, 758, 760, 762, 1042, 1201, 1233, 1325, 1405, 1409, 1410, 1412, 1414], "uniqu": [15, 27, 238, 255, 278, 311, 312, 378, 460, 464, 469, 559, 560, 565, 585, 587, 600, 604, 618, 619, 641, 643, 691, 732, 748, 934, 979, 1047, 1244, 1250, 1251, 1296, 1326, 1343, 1359, 1360, 1363, 1364, 1426], "combin": [15, 61, 102, 204, 207, 379, 380, 385, 411, 412, 416, 418, 423, 575, 598, 600, 604, 678, 691, 891, 892, 928, 973, 1010, 1387, 1408], "type": [15, 70, 93, 95, 97, 100, 101, 102, 103, 104, 110, 165, 208, 241, 242, 243, 244, 247, 266, 267, 269, 270, 271, 273, 274, 276, 282, 283, 296, 301, 302, 303, 308, 309, 315, 323, 350, 351, 429, 496, 549, 550, 551, 555, 584, 585, 587, 588, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 652, 658, 671, 672, 673, 674, 690, 691, 693, 695, 711, 722, 748, 749, 750, 786, 862, 907, 943, 988, 1041, 1043, 1047, 1087, 1091, 1092, 1093, 1094, 1097, 1098, 1099, 1100, 1101, 1102, 1104, 1105, 1110, 1118, 1145, 1147, 1148, 1150, 1152, 1154, 1155, 1157, 1159, 1160, 1163, 1175, 1177, 1178, 1180, 1182, 1183, 1184, 1190, 1191, 1192, 1200, 1201, 1202, 1211, 1213, 1215, 1217, 1222, 1243, 1244, 1245, 1246, 1247, 1248, 1249, 1250, 1252, 1253, 1254, 1255, 1256, 1257, 1259, 1260, 1261, 1262, 1263, 1264, 1273, 1278, 1279, 1281, 1298, 1325, 1326, 1332, 1333, 1336, 1337, 1338, 1342, 1345, 1348, 1349, 1350, 1356, 1357, 1358, 1370, 1371, 1382, 1386, 1390, 1393, 1395, 1404, 1406, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1417, 1425, 1426], "other": [15, 16, 24, 41, 43, 50, 52, 56, 57, 58, 83, 88, 91, 92, 93, 94, 97, 99, 100, 101, 102, 103, 104, 105, 107, 109, 110, 115, 132, 134, 165, 208, 214, 215, 216, 226, 230, 231, 232, 235, 256, 258, 264, 267, 268, 282, 288, 289, 294, 297, 298, 305, 316, 320, 322, 324, 325, 327, 352, 358, 366, 373, 396, 397, 428, 452, 453, 460, 462, 473, 491, 502, 503, 506, 507, 527, 537, 559, 560, 565, 588, 602, 632, 633, 635, 636, 641, 653, 660, 661, 662, 665, 666, 667, 668, 669, 675, 676, 688, 691, 701, 723, 724, 725, 726, 734, 735, 736, 737, 751, 752, 762, 789, 791, 796, 862, 907, 943, 948, 988, 993, 1037, 1038, 1039, 1040, 1042, 1054, 1102, 1103, 1114, 1116, 1133, 1145, 1147, 1151, 1154, 1165, 1174, 1180, 1186, 1194, 1195, 1197, 1198, 1222, 1229, 1269, 1278, 1279, 1281, 1286, 1289, 1291, 1293, 1296, 1302, 1324, 1325, 1326, 1328, 1337, 1338, 1339, 1345, 1348, 1349, 1350, 1382, 1383, 1394, 1396, 1398, 1403, 1404, 1405, 1406, 1407, 1408, 1410, 1411, 1412, 1413, 1414, 1417, 1425, 1426], "produc": [15, 44, 49, 103, 115, 226, 246, 247, 272, 280, 297, 298, 306, 307, 315, 316, 329, 330, 422, 460, 565, 601, 612, 629, 632, 633, 635, 636, 677, 678, 680, 691, 786, 1097, 1102, 1103, 1105, 1165, 1179, 1181, 1189, 1212, 1236, 1280, 1281, 1282, 1283, 1284, 1285, 1286, 1287, 1288, 1392, 1399, 1406, 1408, 1416, 1417], "infer": [15, 695, 1104, 1118, 1358, 1412], "differ": [15, 25, 27, 28, 33, 41, 53, 54, 57, 63, 71, 86, 92, 93, 94, 95, 99, 103, 112, 161, 164, 165, 204, 207, 215, 216, 223, 280, 282, 297, 298, 314, 315, 326, 330, 334, 335, 337, 341, 358, 361, 371, 372, 373, 374, 378, 410, 413, 414, 415, 435, 437, 509, 511, 512, 593, 602, 615, 704, 717, 718, 738, 750, 758, 772, 786, 862, 891, 892, 907, 928, 943, 972, 973, 988, 1010, 1102, 1105, 1133, 1165, 1169, 1170, 1171, 1193, 1198, 1207, 1255, 1269, 1287, 1296, 1326, 1365, 1366, 1382, 1394, 1404, 1405, 1406, 1413, 1414, 1425, 1426], "relat": [15, 34, 67, 92, 93, 95, 99, 100, 115, 129, 132, 220, 230, 297, 366, 370, 586, 588, 619, 688, 762, 767, 795, 1202, 1205, 1269, 1323, 1395, 1402, 1406, 1413, 1416, 1425], "strong": [15, 397, 511, 512, 517, 610, 619, 691, 699, 758, 1408], "weak": [15, 398, 691, 758, 1425], "number_of_nod": [15, 25, 80, 156, 187, 311, 324, 337, 383, 564, 581, 852, 855, 876, 897, 900, 919, 933, 936, 958, 978, 981, 1001, 1154, 1271, 1426], "7482934": 15, "_": [15, 16, 26, 38, 93, 105, 300, 333, 356, 372, 405, 406, 425, 426, 502, 503, 506, 507, 569, 588, 630, 1352, 1354, 1378, 1380, 1411], "edge_type_visual_weight_lookup": 15, "edge_weight": [15, 382, 583], "node_attribut": [15, 691], "edge_attribut": [15, 283, 691, 1101], "summary_graph": [15, 691], "snap_aggreg": [15, 758, 1413], "prefix": [15, 67, 512, 690, 691, 1272, 1326, 1347, 1413, 1421], "aggreg": [15, 511, 512, 691, 786], "summary_po": 15, "8375428": 15, "edge_typ": 15, "get_edge_data": [15, 25, 1411], "163": [15, 17, 238, 297, 298, 306, 307, 329, 453, 752, 1164, 1323], "plot_snap": [15, 17], "support": [16, 52, 77, 92, 93, 96, 100, 101, 102, 103, 226, 308, 322, 339, 340, 342, 343, 356, 373, 410, 411, 412, 418, 419, 464, 494, 496, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 597, 626, 627, 632, 633, 635, 636, 690, 738, 762, 775, 786, 796, 1037, 1038, 1039, 1040, 1114, 1116, 1302, 1326, 1341, 1342, 1344, 1353, 1354, 1355, 1356, 1357, 1358, 1379, 1380, 1381, 1383, 1387, 1394, 1395, 1396, 1398, 1402, 1404, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426], "unsupport": 16, "contain": [16, 25, 34, 45, 65, 69, 71, 88, 99, 102, 104, 114, 115, 151, 152, 157, 158, 165, 166, 167, 168, 172, 175, 176, 177, 180, 188, 189, 193, 195, 199, 207, 212, 214, 220, 226, 236, 237, 238, 240, 241, 243, 245, 248, 249, 252, 253, 255, 256, 257, 258, 259, 260, 264, 266, 267, 270, 277, 278, 280, 281, 290, 293, 294, 299, 315, 320, 322, 338, 344, 346, 347, 350, 352, 353, 355, 356, 357, 358, 360, 373, 377, 379, 380, 381, 388, 400, 408, 414, 415, 427, 432, 433, 437, 440, 457, 481, 482, 494, 495, 498, 499, 500, 502, 503, 506, 507, 509, 510, 512, 513, 514, 516, 564, 568, 572, 574, 589, 593, 596, 599, 602, 621, 624, 631, 632, 652, 656, 658, 660, 661, 662, 687, 688, 689, 695, 723, 724, 725, 726, 749, 786, 796, 853, 854, 856, 857, 862, 863, 864, 865, 868, 869, 870, 871, 877, 878, 882, 884, 887, 892, 898, 899, 901, 902, 907, 908, 909, 910, 913, 914, 921, 923, 925, 928, 934, 935, 937, 938, 943, 944, 945, 946, 949, 950, 951, 952, 959, 960, 963, 965, 968, 973, 979, 980, 982, 983, 988, 989, 990, 991, 994, 995, 1003, 1005, 1007, 1010, 1037, 1038, 1039, 1040, 1041, 1052, 1053, 1054, 1061, 1066, 1085, 1086, 1087, 1094, 1097, 1100, 1102, 1103, 1105, 1106, 1118, 1127, 1140, 1150, 1151, 1152, 1154, 1157, 1164, 1173, 1200, 1201, 1206, 1207, 1208, 1211, 1251, 1286, 1296, 1297, 1298, 1302, 1322, 1323, 1324, 1326, 1331, 1334, 1352, 1356, 1359, 1360, 1363, 1364, 1371, 1378, 1390, 1395, 1403, 1404, 1406, 1407, 1409, 1411, 1412, 1414, 1423, 1425, 1426], "entir": [16, 95, 101, 165, 179, 184, 260, 360, 375, 577, 862, 873, 907, 916, 943, 955, 988, 998, 1038, 1085, 1100, 1225, 1406, 1409], "adopt": [16, 96, 98, 101, 102, 107, 1405, 1414], "lobpcg": [16, 91, 1275, 1276, 1277], "python_exampl": 16, "graph_partit": 16, "categor": [16, 546, 547, 548, 611], "node_typ": [16, 1342, 1356, 1357], "supported_nod": 16, "unsupported_nod": 16, "remove_edges_from": [16, 89, 192, 453, 602, 881, 920, 962, 1002, 1175, 1177, 1222, 1393, 1394, 1412, 1420, 1426], "nbr": [16, 88, 159, 190, 199, 200, 207, 229, 230, 231, 285, 500, 506, 796, 858, 879, 887, 888, 892, 903, 925, 928, 939, 968, 969, 973, 984, 1007, 1010, 1037, 1039, 1040, 1094, 1326, 1404, 1426], "adj": [16, 88, 199, 200, 207, 324, 325, 796, 849, 887, 888, 892, 894, 915, 925, 928, 930, 968, 969, 973, 975, 996, 1007, 1010, 1037, 1039, 1040, 1094, 1326, 1404, 1411, 1417, 1425, 1426], "g_minus_h": 16, "strip": [16, 25, 69, 1215], "_node_color": 16, "_po": 16, "draw_networkx_edg": [16, 25, 26, 27, 28, 33, 35, 38, 39, 40, 41, 44, 46, 68, 83, 1130, 1133, 1134, 1136, 1137, 1411, 1413], "draw_networkx_label": [16, 25, 35, 38, 46, 71, 1130, 1133, 1134, 1135, 1137], "ncl": 16, "undirect": [16, 25, 34, 71, 93, 112, 177, 185, 204, 205, 209, 211, 212, 214, 215, 216, 217, 218, 219, 220, 221, 224, 227, 228, 229, 230, 231, 232, 237, 239, 240, 246, 247, 264, 267, 275, 277, 278, 280, 281, 293, 294, 295, 297, 298, 300, 313, 315, 318, 319, 321, 322, 328, 330, 331, 332, 333, 337, 338, 341, 345, 346, 347, 348, 349, 350, 352, 353, 371, 372, 379, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 428, 430, 431, 437, 439, 440, 450, 463, 464, 465, 466, 467, 478, 479, 480, 481, 482, 485, 486, 487, 488, 490, 491, 492, 500, 559, 560, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 581, 582, 583, 590, 594, 595, 598, 600, 601, 605, 606, 607, 610, 611, 613, 615, 618, 619, 624, 625, 652, 658, 681, 682, 683, 684, 686, 687, 688, 689, 692, 694, 717, 718, 727, 730, 731, 732, 734, 735, 736, 737, 738, 742, 743, 753, 760, 761, 762, 767, 779, 791, 874, 891, 917, 927, 956, 972, 999, 1009, 1036, 1038, 1056, 1060, 1088, 1090, 1098, 1101, 1115, 1133, 1135, 1166, 1167, 1173, 1175, 1182, 1184, 1187, 1189, 1190, 1191, 1193, 1196, 1197, 1198, 1199, 1202, 1206, 1207, 1217, 1219, 1230, 1243, 1244, 1247, 1250, 1251, 1252, 1254, 1259, 1273, 1275, 1276, 1278, 1279, 1282, 1298, 1323, 1326, 1327, 1333, 1341, 1342, 1344, 1351, 1352, 1353, 1354, 1371, 1377, 1378, 1379, 1380, 1381, 1383, 1389, 1390, 1395, 1401, 1402, 1404, 1406, 1408, 1411, 1414, 1417, 1426], "And": [16, 23, 47, 86, 93, 101, 107, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 467, 502, 503, 506, 507, 688, 1296, 1297, 1328, 1408, 1409, 1411, 1416, 1425], "specifi": [16, 24, 25, 62, 102, 151, 152, 157, 158, 167, 184, 185, 193, 207, 222, 223, 226, 232, 236, 238, 240, 241, 243, 244, 246, 247, 248, 260, 264, 266, 267, 268, 269, 271, 273, 276, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 299, 305, 310, 311, 320, 324, 326, 329, 338, 348, 349, 353, 356, 357, 374, 377, 410, 411, 412, 413, 414, 415, 418, 419, 433, 435, 436, 440, 442, 443, 444, 445, 447, 448, 449, 458, 473, 491, 494, 495, 498, 499, 510, 518, 552, 553, 554, 555, 564, 565, 566, 575, 577, 584, 588, 597, 601, 604, 608, 609, 635, 636, 660, 671, 672, 673, 674, 676, 686, 691, 692, 704, 705, 706, 707, 708, 710, 711, 712, 713, 714, 715, 716, 721, 722, 751, 760, 853, 854, 856, 857, 864, 873, 874, 882, 892, 898, 899, 901, 902, 909, 916, 917, 921, 928, 934, 935, 937, 938, 945, 947, 948, 955, 956, 962, 963, 973, 979, 980, 982, 983, 990, 992, 993, 998, 999, 1002, 1003, 1010, 1043, 1061, 1070, 1071, 1072, 1081, 1094, 1095, 1096, 1098, 1099, 1104, 1117, 1130, 1133, 1134, 1135, 1136, 1137, 1151, 1154, 1165, 1175, 1177, 1178, 1181, 1182, 1189, 1193, 1196, 1197, 1198, 1199, 1202, 1207, 1210, 1211, 1212, 1219, 1222, 1235, 1242, 1275, 1276, 1277, 1278, 1279, 1294, 1295, 1296, 1297, 1300, 1315, 1323, 1324, 1326, 1328, 1331, 1334, 1336, 1337, 1338, 1339, 1340, 1341, 1344, 1345, 1348, 1349, 1350, 1356, 1357, 1360, 1363, 1364, 1382, 1393, 1397, 1398, 1399, 1402, 1403, 1404, 1406, 1407, 1412, 1416, 1426], "to_undirect": [16, 25, 69, 796, 1037, 1039, 1040, 1182, 1184, 1404, 1413, 1426], "magenta": 16, "six": 16, "classifi": [16, 512, 684, 750], "four": [16, 23, 47, 86, 99, 102, 165, 263, 585, 587, 692, 862, 907, 943, 988, 1039, 1040, 1164, 1193, 1199, 1211, 1323, 1407, 1408, 1414, 1426], "green": [16, 32, 38, 70, 93, 115, 464, 598, 760, 1302, 1330, 1394, 1412, 1426], "goal": [16, 88, 92, 99, 105, 107, 127, 383, 626, 627, 717, 718, 1042], "g_ex": 16, "m": [16, 25, 28, 30, 31, 63, 65, 67, 91, 93, 96, 102, 106, 110, 112, 128, 181, 191, 201, 209, 211, 212, 219, 227, 231, 235, 236, 238, 239, 240, 241, 243, 244, 248, 257, 258, 259, 263, 272, 274, 275, 278, 280, 282, 284, 293, 294, 296, 300, 301, 302, 308, 309, 315, 316, 317, 330, 338, 341, 343, 345, 352, 355, 356, 361, 362, 370, 380, 383, 385, 412, 429, 431, 432, 433, 451, 462, 479, 494, 498, 499, 509, 510, 511, 512, 519, 545, 555, 569, 582, 584, 585, 587, 588, 606, 614, 619, 625, 652, 658, 659, 684, 686, 691, 692, 706, 748, 749, 761, 762, 775, 872, 880, 889, 953, 961, 970, 1060, 1151, 1155, 1157, 1169, 1175, 1177, 1179, 1181, 1199, 1201, 1202, 1203, 1204, 1205, 1207, 1208, 1209, 1210, 1211, 1213, 1215, 1216, 1218, 1219, 1220, 1222, 1223, 1226, 1229, 1230, 1231, 1233, 1234, 1235, 1240, 1256, 1265, 1269, 1271, 1278, 1279, 1280, 1287, 1288, 1292, 1323, 1387, 1406, 1409, 1426], "node_color_list": 16, "nc": [16, 56], "spectral_layout": [16, 43, 1141, 1399, 1406], "subgraphs_of_g_ex": 16, "removed_edg": 16, "node_color_list_c": 16, "One": [16, 52, 55, 101, 102, 103, 115, 545, 559, 560, 679, 684, 761, 1177, 1186, 1272, 1315, 1326, 1404, 1426], "g_ex_r": 16, "compos": [16, 269, 270, 271, 272, 273, 274, 275, 276, 600, 604, 758, 1400, 1406, 1407, 1417, 1423, 1425], "previous": [16, 91, 108, 112, 322, 614, 1182, 1183, 1184, 1395, 1407, 1417], "store": [16, 25, 39, 53, 54, 55, 57, 67, 86, 93, 97, 101, 102, 110, 158, 219, 220, 283, 290, 345, 346, 347, 431, 470, 471, 472, 473, 474, 475, 494, 495, 498, 499, 502, 503, 506, 507, 509, 510, 585, 587, 615, 660, 664, 667, 719, 733, 739, 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982, 983, 1005, 1412, 1413, 1414, 1425], "hold": [45, 88, 100, 151, 159, 166, 175, 188, 190, 196, 198, 200, 208, 227, 239, 240, 241, 242, 243, 244, 247, 252, 266, 297, 298, 303, 306, 307, 311, 315, 316, 323, 324, 325, 326, 329, 330, 352, 355, 356, 380, 381, 383, 384, 385, 491, 593, 647, 687, 688, 689, 738, 796, 853, 858, 863, 869, 877, 879, 885, 886, 888, 898, 903, 908, 924, 939, 944, 950, 959, 966, 967, 969, 984, 989, 1006, 1020, 1037, 1039, 1040, 1102, 1103, 1105, 1108, 1112, 1115, 1117, 1287, 1288, 1393, 1407, 1409, 1426], "call": [45, 55, 58, 63, 93, 94, 97, 101, 102, 112, 114, 132, 141, 164, 168, 184, 189, 206, 212, 230, 231, 244, 249, 338, 341, 346, 347, 394, 410, 412, 414, 416, 417, 418, 419, 426, 450, 452, 453, 464, 470, 491, 492, 494, 498, 499, 502, 503, 506, 507, 509, 510, 517, 525, 530, 535, 540, 545, 555, 584, 586, 588, 606, 615, 652, 658, 671, 672, 673, 674, 678, 691, 732, 760, 762, 767, 786, 865, 873, 878, 910, 916, 946, 948, 955, 960, 991, 993, 998, 1036, 1038, 1041, 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1425, 1426], "carol": [45, 1255], "getaddress": 45, "parseaddr": 45, "recip": [45, 660, 667], "doc": [45, 93, 99, 101, 106, 165, 202, 204, 282, 566, 620, 749, 862, 890, 891, 907, 926, 927, 943, 971, 972, 988, 1008, 1009, 1042, 1105, 1197, 1373, 1375, 1376, 1389, 1396, 1406, 1407, 1408, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1420, 1421, 1422, 1424, 1425], "mbox_graph": 45, "pars": [45, 65, 266, 1332, 1336, 1348, 1349, 1351, 1352, 1370, 1374, 1377, 1378, 1381, 1383, 1385, 1398, 1406, 1408, 1414, 1419, 1425], "msg": [45, 93, 103], "source_nam": 45, "source_addr": 45, "recipi": 45, "tos": 45, "get_al": 45, "cc": [45, 71, 127, 142, 143, 322, 423, 425, 1413], "resent_to": 45, "resent": 45, "resent_cc": 45, "all_recipi": 45, "now": [45, 54, 75, 76, 93, 97, 101, 132, 380, 754, 762, 963, 1003, 1177, 1217, 1278, 1279, 1393, 1394, 1395, 1396, 1397, 1398, 1399, 1401, 1402, 1404, 1405, 1406, 1407, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1425, 1426], "mail": [45, 92, 93, 94, 99, 100, 103, 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199, 310, 460, 586, 791, 887, 925, 968, 1007, 1217, 1234, 1269, 1326, 1403, 1404], "approach": [52, 55, 99, 101, 103, 104, 107, 115, 341, 345, 462, 464, 466, 500, 519, 616, 678, 1094, 1175, 1188, 1202, 1222, 1407, 1413], "segment": [52, 55, 338], "major": [52, 95, 98, 99, 100, 102, 103, 104, 106, 107, 1393, 1394, 1403, 1404, 1407], "studi": [52, 91, 110, 606, 1192, 1196, 1323, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425], "topologi": [52, 55, 435, 436, 512, 681, 683, 748, 1202, 1217, 1225, 1229, 1233, 1241, 1326], "encod": [52, 55, 58, 67, 99, 141, 249, 267, 268, 619, 758, 775, 1326, 1333, 1334, 1337, 1338, 1339, 1340, 1341, 1344, 1345, 1348, 1349, 1350, 1354, 1355, 1358, 1363, 1368, 1371, 1372, 1375, 1376, 1382, 1406, 1407, 1412], "angular": [52, 55], "inform": [52, 66, 92, 93, 99, 100, 101, 102, 103, 107, 111, 112, 121, 132, 159, 165, 200, 202, 204, 220, 226, 230, 231, 249, 301, 302, 303, 308, 309, 314, 323, 324, 325, 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1407, 1411, 1425], "fiona": [54, 57], "level": [54, 57, 101, 103, 104, 106, 111, 112, 115, 125, 165, 220, 322, 334, 336, 374, 380, 381, 387, 389, 390, 394, 423, 427, 640, 691, 770, 786, 862, 907, 943, 988, 1012, 1013, 1018, 1019, 1020, 1021, 1022, 1094, 1108, 1155, 1202, 1207, 1208, 1236, 1296, 1323, 1328, 1396, 1399, 1407, 1412, 1413, 1414], "interfac": [54, 57, 58, 75, 76, 96, 98, 99, 101, 102, 107, 109, 110, 184, 429, 496, 673, 758, 761, 762, 780, 873, 916, 955, 998, 1042, 1044, 1326, 1328, 1393, 1396, 1398, 1402, 1404, 1405, 1406, 1409, 1413, 1414, 1426], "kind": [54, 57, 58, 92, 93, 94, 99, 208, 466, 722, 1202, 1326, 1383], "read_fil": [54, 55, 57, 58], "cholera_cas": [54, 57], "gpkg": [54, 56, 57], "correctli": [54, 164, 324, 325, 1393, 1404, 1406, 1411, 1412, 1419], "construct": [54, 55, 56, 57, 58, 67, 94, 102, 227, 229, 230, 231, 232, 269, 273, 276, 352, 423, 450, 460, 513, 545, 546, 547, 548, 552, 553, 554, 556, 557, 558, 609, 685, 695, 708, 716, 732, 1046, 1047, 1052, 1053, 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1215, 1222], "lo": 91, "alamo": 91, "nation": [91, 92, 457, 720], "laboratori": 91, "pi": [91, 653, 1114], "program": [91, 105, 110, 362, 455, 488, 490, 678, 1119, 1120, 1125, 1226, 1302, 1324, 1326, 1328, 1414], "offic": [91, 1267], "complex": [91, 94, 101, 105, 210, 217, 229, 230, 231, 239, 240, 274, 290, 293, 294, 300, 314, 327, 330, 331, 332, 333, 337, 346, 347, 355, 356, 371, 372, 376, 385, 386, 423, 434, 438, 452, 453, 494, 500, 519, 520, 521, 574, 616, 619, 625, 659, 692, 698, 699, 749, 1120, 1126, 1175, 1179, 1196, 1197, 1198, 1341, 1342, 1344, 1381, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425], "depart": [91, 494], "physic": [91, 110, 230, 236, 241, 244, 248, 326, 332, 333, 355, 356, 358, 378, 383, 386, 438, 485, 486, 487, 625, 1169, 1170, 1171, 1193, 1222, 1229, 1233], "crutchfield": 91, "institut": [91, 112, 214, 215, 216, 220], "discoveri": [91, 670, 675, 676, 690], "madison": 91, "jessica": 91, "flack": 91, 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107, 566, 1278, 1279, 1395, 1403], "trigger": 93, "servic": [93, 106, 110, 1383], "pass": [93, 99, 102, 103, 115, 152, 157, 158, 195, 206, 208, 229, 239, 240, 252, 253, 257, 260, 297, 298, 306, 307, 315, 326, 330, 411, 412, 416, 417, 418, 419, 470, 502, 503, 506, 507, 586, 593, 670, 678, 723, 724, 725, 726, 749, 751, 753, 796, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 959, 965, 980, 982, 983, 1005, 1037, 1038, 1039, 1040, 1127, 1133, 1135, 1154, 1193, 1197, 1269, 1278, 1279, 1298, 1300, 1363, 1399, 1402, 1404, 1406, 1408, 1409, 1412, 1413, 1414, 1415, 1416, 1419, 1426], "fail": [93, 100, 193, 195, 311, 324, 464, 468, 497, 564, 566, 628, 629, 630, 882, 884, 921, 923, 931, 963, 965, 976, 1003, 1005, 1039, 1040, 1043, 1326, 1406, 1407, 1411, 1412, 1414, 1419, 1421, 1423, 1425], "why": [93, 104, 115, 679], "inspect": [93, 101, 1047, 1296, 1417], "inlin": [93, 1420], "ve": [93, 96, 1326], "learn": [93, 94, 103, 111, 342, 511, 512, 590, 591, 592, 770, 1326], "overal": 93, 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1407], "st": [141, 415, 417], "cut": [141, 222, 223, 293, 377, 382, 387, 389, 390, 394, 411, 412, 414, 415, 416, 417, 419, 427, 428, 429, 442, 443, 444, 445, 447, 494, 495, 498, 499, 500, 502, 503, 506, 507, 509, 510, 619, 758, 760, 1038, 1066, 1115, 1262, 1325, 1395, 1402, 1406, 1413], "refin": [143, 215, 423, 438], "auxgraph": [143, 423], "node_partit": 144, "permut": [144, 368, 452, 453, 455, 466, 748, 1285, 1320, 1321], "containin": 144, "frozenset": [144, 267, 339, 383, 586, 588, 752, 1165, 1333, 1337, 1338, 1412], "abc": [144, 545, 1154, 1206, 1303, 1412, 1413], "interchang": [144, 362], "bool": [145, 146, 148, 149, 165, 168, 171, 176, 184, 189, 196, 204, 208, 232, 237, 238, 242, 243, 245, 249, 250, 258, 265, 266, 267, 268, 272, 275, 286, 287, 288, 291, 294, 295, 296, 297, 298, 299, 301, 302, 305, 306, 307, 308, 309, 310, 314, 315, 322, 324, 325, 326, 327, 330, 343, 350, 355, 362, 393, 394, 395, 396, 397, 398, 439, 454, 462, 463, 467, 479, 480, 488, 489, 491, 494, 498, 499, 509, 510, 513, 514, 515, 516, 517, 518, 520, 521, 522, 545, 562, 564, 578, 579, 580, 581, 588, 613, 614, 616, 617, 622, 623, 625, 640, 652, 663, 673, 679, 685, 690, 696, 698, 699, 700, 704, 708, 719, 723, 724, 725, 726, 728, 730, 733, 734, 735, 736, 737, 738, 740, 741, 742, 743, 862, 865, 867, 870, 873, 878, 885, 891, 907, 910, 912, 916, 927, 931, 943, 946, 948, 951, 955, 960, 966, 972, 976, 988, 991, 993, 998, 1039, 1040, 1045, 1057, 1068, 1070, 1071, 1072, 1084, 1091, 1097, 1116, 1133, 1134, 1135, 1136, 1169, 1179, 1185, 1189, 1209, 1211, 1212, 1213, 1215, 1224, 1228, 1230, 1231, 1232, 1275, 1276, 1277, 1278, 1279, 1282, 1295, 1296, 1307, 1309, 1312, 1335, 1336, 1337, 1339, 1341, 1342, 1344, 1353, 1354, 1355, 1356, 1357, 1358, 1360, 1364, 1379, 1380], "account": [145, 148, 398, 448, 749, 761, 1270, 1393, 1413], "graph_nod": [145, 148], "subgraph_nod": [145, 148], "find_isomorph": [147, 150], "induc": [148, 167, 199, 211, 226, 342, 388, 392, 406, 427, 436, 437, 470, 487, 494, 495, 498, 499, 502, 503, 506, 507, 509, 510, 512, 586, 589, 752, 761, 762, 864, 887, 909, 925, 945, 968, 990, 1007, 1038, 1061, 1066, 1087, 1102, 1103, 1105, 1189, 1283, 1284, 1393], "u_of_edg": [151, 853, 898], "v_of_edg": [151, 853, 898], "capac": [151, 265, 296, 301, 302, 303, 308, 309, 323, 411, 412, 415, 416, 417, 418, 419, 430, 431, 494, 495, 496, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 758, 853, 898, 934, 979, 1335, 1402], "342": [151, 853, 898, 934, 979, 1255], "ebunch_to_add": [152, 158, 854, 857, 899, 902, 935, 938, 980, 983], "add_weighted_edges_from": [152, 229, 230, 231, 508, 581, 630, 657, 659, 721, 854, 899, 935, 980, 1070, 1326, 1404, 1407, 1426], "runtimeerror": [152, 157, 158, 195, 464, 465, 466, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005], "happen": [152, 157, 158, 195, 380, 584, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005, 1403, 1404, 1425], "iterator_of_edg": [152, 158, 854, 857, 899, 902, 935, 938, 980, 983], "wn2898": [152, 854, 899, 935, 980], "wrong": [152, 157, 158, 722, 854, 856, 857, 899, 901, 902, 935, 937, 938, 980, 982, 983, 1406, 1411, 1416, 1425], "start_nod": [153, 154, 155], "end_nod": [153, 154, 155], "reference_neighbor": [153, 154], "half": [153, 154, 155, 164, 177, 183, 206, 297, 298, 615, 653], "clockwis": [153, 154, 169, 182, 197, 615], "networkxexcept": [153, 154, 161, 331, 588, 593, 724, 726, 1043, 1110, 1138, 1180, 1325], "add_half_edge_cw": [153, 155, 164, 615], "connect_compon": [153, 154, 155, 615], "add_half_edge_first": [153, 154, 164, 615], "add_half_edge_ccw": [154, 155, 164, 615], "node_for_ad": [156, 855, 900, 936, 981], "mutabl": [156, 855, 900, 936, 981, 1061, 1066, 1082, 1085, 1086], "hash": [156, 511, 512, 758, 855, 900, 936, 981, 1324, 1325, 1414, 1426], "hello": [156, 157, 855, 856, 900, 901, 936, 937, 981, 982, 1303], "k3": [156, 157, 855, 856, 900, 901, 936, 937, 981, 982, 1217], "utm": [156, 855, 900, 936, 981], "382871": [156, 855, 900, 936, 981], "3972649": [156, 855, 900, 936, 981], "nodes_for_ad": [157, 856, 901, 937, 982], "iterator_of_nod": [157, 195, 856, 884, 901, 923, 937, 965, 982, 1005], "datadict": [159, 190, 200, 207, 734, 736, 858, 879, 888, 892, 903, 928, 939, 969, 973, 1010, 1084, 1312, 1326], "foovalu": [159, 190, 200, 858, 879, 888, 903, 939, 969], "nbrdict": [160, 859, 904, 940, 985, 1019, 1094], "fulfil": [161, 615], "cw": [161, 615], "ccw": [161, 615], "planar": [161, 614, 616, 617, 758, 1110, 1138, 1243, 1246, 1247, 1249, 1325, 1409, 1410], "first_nbr": [161, 615], "invalid": [161, 615, 1413], "alter": [163, 861, 906, 942, 987], "afterward": 164, "as_view": [165, 202, 204, 862, 890, 891, 907, 926, 927, 943, 971, 972, 988, 1008, 1009, 1089, 1090], "shallow": [165, 202, 204, 284, 285, 286, 287, 288, 862, 890, 891, 907, 926, 927, 943, 971, 972, 988, 1008, 1009, 1394], "deepcopi": [165, 202, 204, 862, 890, 891, 907, 926, 927, 943, 971, 972, 988, 1008, 1009, 1409], "__class__": [165, 199, 862, 887, 907, 925, 943, 968, 988, 1007, 1404, 1407, 1409, 1410, 1411], "fresh": [165, 862, 907, 943, 988, 1404], "inspir": [165, 230, 231, 342, 681, 862, 907, 943, 988, 1226, 1323, 1404], "deep": [165, 202, 204, 862, 890, 891, 907, 926, 927, 943, 971, 972, 988, 1008, 1009, 1265, 1394], "degreeview": [166, 863, 908, 944, 950, 989, 1404, 1426], "didegreeview": [166, 863], "outedgeview": [168, 189, 467, 468, 613, 747, 750, 865, 878, 1035, 1083, 1404, 1418], "ddict": [168, 176, 184, 189, 865, 870, 873, 878, 910, 916, 946, 951, 955, 960, 991, 998], "in_edg": [168, 189, 865, 878, 946, 960, 1404, 1406, 1407], "out_edg": [168, 865, 946, 1062, 1404, 1406, 1407, 1426], "quietli": [168, 189, 865, 878, 910, 946, 960, 991, 1087, 1426], "outedgedataview": [168, 189, 865, 878, 1404, 1411], "set_data": 169, "edge_dict": [170, 866, 911, 947, 992], "safe": [170, 866, 911, 1404, 1412], "edge_ind": [171, 867, 912, 948, 993], "data_dictionari": [171, 867, 912], "simpler": [172, 184, 868, 873, 913, 916, 949, 955, 994, 998, 1406, 1407, 1417], "indegreeview": [175, 869, 1404], "deg": [175, 188, 243, 259, 356, 361, 685, 869, 877, 950, 959, 1165, 1179, 1222, 1404], "inedgeview": [176, 870, 1404], "inedgedataview": [176, 870], "silent": [180, 193, 195, 320, 871, 882, 884, 914, 921, 923, 952, 963, 965, 995, 1003, 1005, 1085, 1086, 1127, 1353, 1354, 1359, 1363, 1406, 1413], "niter": [180, 681, 682, 683, 684, 851, 871, 896, 914, 932, 952, 977, 995, 1414], "__iter__": [180, 871, 914, 952, 995, 1303], "nodedata": [184, 873, 916, 955, 998], "5pm": [184, 796, 873, 916, 955, 998, 1037, 1039, 1040, 1394, 1426], "Not": [184, 379, 432, 433, 434, 435, 436, 437, 438, 476, 873, 916, 955, 998, 1117, 1216], "nedg": [185, 588, 874, 917, 956, 999], "__len__": [186, 187, 875, 876, 918, 919, 957, 958, 1000, 1001], "outdegreeview": [188, 877], "Will": [193, 362, 605, 607, 610, 882, 921, 963, 1003, 1404, 1414], "get_data": [197, 616], "inplac": [199, 690, 887, 925, 968, 1007, 1066, 1393], "reduct": [199, 469, 618, 786, 887, 925, 968, 1007, 1066, 1320, 1321, 1413, 1414], "sg": [199, 887, 925, 968, 1007], "largest_wcc": [199, 887, 925, 968, 1007], "is_multigraph": [199, 758, 887, 925, 968, 1007, 1154, 1412], "keydict": [199, 207, 887, 892, 925, 928, 968, 973, 1007, 1010, 1039, 1040], "contrast": [202, 204, 301, 302, 308, 309, 890, 891, 926, 927, 971, 972, 1008, 1009, 1066, 1233, 1241, 1426], "reciproc": [204, 299, 320, 322, 356, 411, 430, 447, 476, 620, 758, 891, 972, 1325, 1416, 1425], "mark_half_edg": 206, "li": [206, 619, 670, 675, 685, 775, 1207, 1210, 1425], "straightforward": [207, 892, 928, 973, 1010], "slightli": [207, 326, 437, 520, 521, 581, 892, 928, 973, 1010, 1165, 1326, 1404, 1407, 1412, 1414, 1425], "singleton": [207, 588, 892, 928, 973, 1010, 1218, 1251, 1407], "preserve_attr": [208, 723, 724, 725, 726], "optimum": [208, 231, 583, 720, 722, 791, 1395, 1406], "arboresc": [208, 460, 719, 720, 722, 724, 726, 740, 743, 758, 1272, 1395, 1406], "span": [208, 226, 227, 228, 295, 508, 618, 619, 624, 719, 720, 722, 724, 726, 732, 733, 734, 735, 736, 737, 738, 758, 1394, 1397, 1406, 1407, 1420], "max_ind_cliqu": 209, "networkxnotimpl": [209, 210, 211, 212, 220, 224, 227, 293, 294, 295, 318, 319, 321, 328, 343, 379, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 403, 404, 405, 406, 407, 422, 424, 425, 426, 427, 429, 455, 457, 458, 459, 460, 468, 481, 482, 500, 589, 590, 608, 680, 732, 1043, 1216, 1275, 1276, 1298, 1325, 1353, 1354, 1379, 1407, 1408], "boppana": [209, 211, 212], "halld\u00f3rsson": [209, 211, 212], "1992": [209, 211, 212, 517, 518, 1407], "exclud": [209, 211, 212, 215, 216, 261, 262, 453, 688, 719, 723, 724, 725, 726, 733, 751, 1036, 1038, 1088, 1217, 1412], "180": [209, 211, 212, 238], "196": [209, 211, 212], "heurist": [210, 220, 228, 233, 234, 377, 380, 381, 427, 494, 509, 626, 627, 652, 663, 703, 758, 1173, 1320, 1321, 1325, 1395, 1408, 1412, 1413], "max_cliqu": 210, "rigor": 210, "pattabiraman": 210, "bharath": 210, "massiv": [210, 217], "448": 210, "1080": [210, 297, 298, 306, 307, 329], "15427951": 210, "986778": 210, "apx": [211, 212], "subseteq": [211, 280, 289, 618, 675], "omega": [211, 758, 782, 1414], "maximum_cliqu": 211, "1007": [211, 226, 296, 301, 302, 303, 308, 309, 323, 324, 325, 341, 431, 451, 498, 574, 1144, 1181], "bf01994876": 211, "iset": 212, "trial": [213, 230, 231, 1195, 1237, 1238], "estim": [213, 224, 297, 306, 313, 564, 625, 626, 627, 782, 1280, 1407], "coeffici": [213, 248, 260, 261, 262, 263, 289, 355, 356, 358, 570, 618, 619, 625, 682, 684, 778, 782, 1397, 1398, 1399, 1406, 1413], "fraction": [213, 257, 259, 286, 289, 297, 299, 304, 306, 315, 317, 318, 319, 321, 322, 326, 328, 330, 356, 358, 359, 519, 1165, 1234], "schank": 213, "thoma": [213, 751, 1407, 1409, 1413], "dorothea": [213, 1168], "wagner": [213, 429, 758, 1168, 1402, 1406], "universit\u00e4t": 213, "karlsruh": 213, "fakult\u00e4t": 213, "f\u00fcr": 213, "informatik": [213, 412], "5445": 213, "ir": [213, 606], "1000001239": 213, "erdos_renyi_graph": [213, 1224, 1232, 1326, 1406, 1426], "214": 213, "cutoff": [214, 215, 310, 326, 383, 410, 411, 412, 418, 419, 494, 495, 498, 499, 510, 637, 638, 640, 641, 642, 643, 644, 647, 648, 649, 656, 660, 661, 662, 667, 668, 669, 677, 678, 1234, 1398, 1402, 1406, 1413, 1416, 1424, 1425], "distinct": [214, 215, 255, 281, 288, 352, 391, 452, 453, 460, 578, 595, 608, 618, 700, 701, 734, 735, 736, 737, 789, 1150, 1244, 1271, 1323, 1326, 1328, 1395, 1417], "nonadjac": [214, 215, 480, 584, 585, 587], "cutset": [214, 215, 414, 415, 416, 417, 427, 428, 500, 506, 758], "menger": [214, 215, 216], "theorem": [214, 215, 216, 220, 235, 281, 311, 312, 322, 411, 506, 507, 514, 517, 518, 618, 1190, 1205], "local_node_connect": [214, 216, 408, 409, 410, 411, 413], "node_connect": [214, 215, 409, 410, 411, 412, 414, 415, 416, 417, 419, 427, 428, 1402], "dougla": [214, 215, 216, 220, 1413, 1425], "035": [214, 215, 216, 220], "eclect": [214, 215, 216], "ss": [214, 215, 216], "uci": [214, 215, 216, 467, 704, 706, 707, 708, 710, 734, 736], "drwhite": [214, 215, 216], "pprint": [214, 577, 711], "all_pairs_node_connect": [215, 216, 1402, 1424, 1425], "bf": [215, 216, 217, 363, 588, 704, 706, 707, 708, 717, 1397, 1401, 1406, 1409, 1412, 1413], "lose": [215, 796, 1037, 1039, 1040], "accuraci": [215, 312, 786], "platon": [215, 216, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 1245, 1248, 1254, 1257, 1261, 1263], "octahedr": [215, 216, 1257], "approx": [215, 216, 227, 229, 230, 231, 1413], "octahedral_graph": [215, 216], "vari": [217, 238, 243, 373, 378, 569, 695], "sweep": [217, 1412], "dsweep": 217, "a_1": [217, 477], "a_2": 217, "magnien": [217, 260, 261, 262, 289], "cl\u00e9menc": [217, 260, 261, 262, 289], "matthieu": [217, 260, 261, 262, 274, 289], "latapi": [217, 260, 261, 262, 274, 289], "michel": 217, "habib": 217, "empir": 217, "tight": 217, "jea": 217, "0904": 217, "2728": 217, "crescenzi": 217, "pierluigi": 217, "roberto": 217, "grossi": 217, "leonardo": 217, "lanzi": 217, "andrea": [217, 1165, 1413], "marino": 217, "symposium": [217, 619, 1186, 1195, 1239], "berlin": [217, 520, 521, 1413], "heidelberg": [217, 520, 521], "ut": 217, "ee": [217, 313], "mtat": 217, "2014_fall": 217, "domin": [218, 219, 311, 410, 414, 481, 482, 483, 484, 758, 1325, 1395, 1400, 1406, 1407], "opt": [218, 221, 1425], "min_weight_dominating_set": 219, "vazirani": [219, 221], "vijai": [219, 221, 517], "min_dens": 220, "95": [220, 590, 1283, 1284, 1382], "nest": [220, 427, 728, 730, 791, 1038, 1045, 1061, 1094, 1296, 1308, 1348, 1355, 1356, 1357, 1358, 1383, 1406], "forth": [220, 427], "relax": [220, 227, 1171, 1413], "narrow": [220, 1165], "whitnei": 220, "bicompon": [220, 387, 389, 390, 394], "ferraro": [220, 427], "cohes": [220, 427, 437], "1503": [220, 427], "04476v1": [220, 427], "santaf": 220, "ind": 220, "embedded": [220, 305, 427], "sociolog": [220, 427, 748], "103": [220, 427, 1222, 1288, 1292], "2307": 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"mentored-projects"]], "Pedagogical Interactive Notebooks for Algorithms Implemented in NetworkX": [[105, "pedagogical-interactive-notebooks-for-algorithms-implemented-in-networkx"]], "Completed Projects": [[105, "completed-projects"]], "Release Process": [[106, "release-process"]], "Roadmap": [[107, "roadmap"]], "Installation": [[107, "installation"]], "Sustainability": [[107, "sustainability"]], "Performance": [[107, "performance"]], "Documentation": [[107, "documentation"], [1406, "documentation"], [1406, "id71"], [1406, "id75"]], "Linear Algebra": [[107, "linear-algebra"]], "Interoperability": [[107, "interoperability"]], "Visualization": [[107, "visualization"]], "Mission and Values": [[109, "mission-and-values"]], "Our mission": [[109, "our-mission"]], "Our values": [[109, "our-values"]], "Software for Complex Networks": [[110, "software-for-complex-networks"]], "Citing": [[110, "citing"]], "Audience": [[110, "audience"]], "Python": [[110, "python"]], "License": [[110, "license"]], "Bibliography": [[110, "bibliography"]], "Install": [[111, "install"]], "Install the released version": [[111, "install-the-released-version"]], "Install the development version": [[111, "install-the-development-version"]], "Extra packages": [[111, "extra-packages"]], "Test a source distribution": [[111, "test-a-source-distribution"]], "Test an installed package": [[111, "test-an-installed-package"]], "Approximations and Heuristics": [[112, "module-networkx.algorithms.approximation"]], "Connectivity": [[112, "module-networkx.algorithms.approximation.connectivity"], [126, "connectivity"], [127, "module-networkx.algorithms.connectivity"]], "K-components": [[112, "module-networkx.algorithms.approximation.kcomponents"]], "Clique": [[112, "module-networkx.algorithms.approximation.clique"], [121, "module-networkx.algorithms.clique"]], "Clustering": [[112, "module-networkx.algorithms.approximation.clustering_coefficient"], [115, "module-networkx.algorithms.bipartite.cluster"], [122, "module-networkx.algorithms.cluster"]], "Distance Measures": [[112, "module-networkx.algorithms.approximation.distance_measures"], [134, "module-networkx.algorithms.distance_measures"]], "Dominating Set": [[112, "module-networkx.algorithms.approximation.dominating_set"]], "Matching": [[112, "module-networkx.algorithms.approximation.matching"], [115, "module-networkx.algorithms.bipartite.matching"], [766, "module-networkx.algorithms.matching"]], "Ramsey": [[112, "module-networkx.algorithms.approximation.ramsey"]], "Steiner Tree": [[112, "module-networkx.algorithms.approximation.steinertree"]], "Traveling Salesman": [[112, "module-networkx.algorithms.approximation.traveling_salesman"]], "Travelling Salesman Problem (TSP)": [[112, "travelling-salesman-problem-tsp"]], "Treewidth": [[112, "module-networkx.algorithms.approximation.treewidth"]], "Vertex Cover": [[112, "module-networkx.algorithms.approximation.vertex_cover"]], "Max Cut": [[112, 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"module-networkx.generators.spectral_graph_forge"]], "Redundancy": [[115, "module-networkx.algorithms.bipartite.redundancy"]], "Centrality": [[115, "module-networkx.algorithms.bipartite.centrality"], [118, "module-networkx.algorithms.centrality"]], "Generators": [[115, "module-networkx.algorithms.bipartite.generators"]], "Covering": [[115, "module-networkx.algorithms.bipartite.covering"], [129, "module-networkx.algorithms.covering"]], "Boundary": [[116, "module-networkx.algorithms.boundary"]], "Bridges": [[117, "module-networkx.algorithms.bridges"]], "Degree": [[118, "degree"]], "Eigenvector": [[118, "eigenvector"]], "Closeness": [[118, "closeness"]], "Current Flow Closeness": [[118, "current-flow-closeness"]], "(Shortest Path) Betweenness": [[118, "shortest-path-betweenness"]], "Current Flow Betweenness": [[118, "current-flow-betweenness"]], "Communicability Betweenness": [[118, "communicability-betweenness"]], "Group Centrality": [[118, "group-centrality"]], "Load": [[118, "load"]], 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"effective_size": [[688, "effective-size"]], "local_constraint": [[689, "local-constraint"]], "dedensify": [[690, "dedensify"]], "snap_aggregation": [[691, "snap-aggregation"]], "connected_double_edge_swap": [[692, "connected-double-edge-swap"]], "directed_edge_swap": [[693, "directed-edge-swap"]], "double_edge_swap": [[694, "double-edge-swap"]], "find_threshold_graph": [[695, "find-threshold-graph"]], "is_threshold_graph": [[696, "is-threshold-graph"]], "hamiltonian_path": [[697, "hamiltonian-path"]], "is_reachable": [[698, "is-reachable"]], "is_tournament": [[700, "is-tournament"]], "random_tournament": [[701, "random-tournament"]], "score_sequence": [[702, "score-sequence"]], "bfs_beam_edges": [[703, "bfs-beam-edges"]], "bfs_edges": [[704, "bfs-edges"]], "bfs_layers": [[705, "bfs-layers"]], "bfs_predecessors": [[706, "bfs-predecessors"]], "bfs_successors": [[707, "bfs-successors"]], "bfs_tree": [[708, "bfs-tree"]], "descendants_at_distance": [[709, "descendants-at-distance"]], "dfs_edges": [[710, "dfs-edges"]], "dfs_labeled_edges": [[711, "dfs-labeled-edges"]], "dfs_postorder_nodes": [[712, "dfs-postorder-nodes"]], "dfs_predecessors": [[713, "dfs-predecessors"]], "dfs_preorder_nodes": [[714, "dfs-preorder-nodes"]], "dfs_successors": [[715, "dfs-successors"]], "dfs_tree": [[716, "dfs-tree"]], "edge_bfs": [[717, "edge-bfs"]], "edge_dfs": [[718, "edge-dfs"]], "networkx.algorithms.tree.branchings.ArborescenceIterator": [[719, "networkx-algorithms-tree-branchings-arborescenceiterator"]], "networkx.algorithms.tree.branchings.Edmonds": [[720, "networkx-algorithms-tree-branchings-edmonds"]], "branching_weight": [[721, "branching-weight"]], "greedy_branching": [[722, "greedy-branching"]], "maximum_branching": [[723, "maximum-branching"]], "maximum_spanning_arborescence": [[724, "maximum-spanning-arborescence"]], "minimum_branching": [[725, "minimum-branching"]], "minimum_spanning_arborescence": [[726, "minimum-spanning-arborescence"]], "NotATree": [[727, "notatree"]], "from_nested_tuple": [[728, "from-nested-tuple"]], "from_prufer_sequence": [[729, "from-prufer-sequence"]], "to_nested_tuple": [[730, "to-nested-tuple"]], "to_prufer_sequence": [[731, "to-prufer-sequence"]], "junction_tree": [[732, "junction-tree"]], "networkx.algorithms.tree.mst.SpanningTreeIterator": [[733, "networkx-algorithms-tree-mst-spanningtreeiterator"]], "maximum_spanning_edges": [[734, "maximum-spanning-edges"]], "maximum_spanning_tree": [[735, "maximum-spanning-tree"]], "minimum_spanning_edges": [[736, "minimum-spanning-edges"]], "minimum_spanning_tree": [[737, "minimum-spanning-tree"]], "random_spanning_tree": [[738, "random-spanning-tree"]], "join": [[739, "join"]], "is_arborescence": [[740, "is-arborescence"]], "is_branching": [[741, "is-branching"]], "is_forest": [[742, "is-forest"]], "is_tree": [[743, "is-tree"]], "all_triads": [[744, "all-triads"]], "all_triplets": [[745, "all-triplets"]], "is_triad": [[746, "is-triad"]], "random_triad": [[747, "random-triad"]], "triad_type": [[748, "triad-type"]], "triadic_census": [[749, "triadic-census"]], "triads_by_type": [[750, "triads-by-type"]], "closeness_vitality": [[751, "closeness-vitality"]], "voronoi_cells": [[752, "voronoi-cells"]], "wiener_index": [[753, "wiener-index"]], "Graph Hashing": [[754, "module-networkx.algorithms.graph_hashing"]], "Graphical degree sequence": [[755, "module-networkx.algorithms.graphical"]], "Hierarchy": [[756, "module-networkx.algorithms.hierarchy"]], "Hybrid": [[757, "module-networkx.algorithms.hybrid"]], "Isolates": [[759, "module-networkx.algorithms.isolate"]], "Isomorphism": [[760, "isomorphism"]], "VF2++": [[760, "module-networkx.algorithms.isomorphism.vf2pp"]], "VF2++ Algorithm": [[760, "vf2-algorithm"]], "Tree Isomorphism": [[760, "module-networkx.algorithms.isomorphism.tree_isomorphism"]], "Advanced Interfaces": [[760, "advanced-interfaces"]], "ISMAGS Algorithm": [[761, "module-networkx.algorithms.isomorphism.ismags"]], "Notes": [[761, "notes"], [762, "notes"]], "ISMAGS object": [[761, "ismags-object"]], "VF2 Algorithm": [[762, "module-networkx.algorithms.isomorphism.isomorphvf2"]], "Subgraph Isomorphism": [[762, "subgraph-isomorphism"]], "Graph Matcher": [[762, "graph-matcher"]], "DiGraph Matcher": [[762, "digraph-matcher"]], "Match helpers": [[762, "match-helpers"]], "Link Analysis": [[763, "link-analysis"]], "PageRank": [[763, "module-networkx.algorithms.link_analysis.pagerank_alg"]], "Hits": [[763, "module-networkx.algorithms.link_analysis.hits_alg"]], "Link Prediction": [[764, "module-networkx.algorithms.link_prediction"]], "Lowest Common Ancestor": [[765, "module-networkx.algorithms.lowest_common_ancestors"]], "Minors": [[767, "module-networkx.algorithms.minors"]], "Maximal independent set": [[768, "module-networkx.algorithms.mis"]], "Moral": [[769, "module-networkx.algorithms.moral"]], "Node Classification": [[770, "module-networkx.algorithms.node_classification"]], "non-randomness": [[771, "module-networkx.algorithms.non_randomness"]], "Operators": [[772, "operators"]], "Planar Drawing": [[773, "module-networkx.algorithms.planar_drawing"]], "Planarity": [[774, "module-networkx.algorithms.planarity"]], "Graph Polynomials": [[775, "module-networkx.algorithms.polynomials"]], "Reciprocity": [[776, "module-networkx.algorithms.reciprocity"]], "Regular": [[777, "module-networkx.algorithms.regular"]], "Rich Club": [[778, "module-networkx.algorithms.richclub"]], "Shortest Paths": [[779, "module-networkx.algorithms.shortest_paths.generic"]], "Advanced Interface": [[779, "module-networkx.algorithms.shortest_paths.unweighted"]], "Dense Graphs": [[779, "module-networkx.algorithms.shortest_paths.dense"]], "A* Algorithm": [[779, "module-networkx.algorithms.shortest_paths.astar"]], "Similarity Measures": [[780, "module-networkx.algorithms.similarity"]], "Simple Paths": [[781, "module-networkx.algorithms.simple_paths"]], "Small-world": [[782, "module-networkx.algorithms.smallworld"]], "s metric": [[783, "module-networkx.algorithms.smetric"]], "Sparsifiers": [[784, "module-networkx.algorithms.sparsifiers"]], "Structural holes": [[785, "module-networkx.algorithms.structuralholes"]], "Summarization": [[786, "module-networkx.algorithms.summarization"]], "Swap": [[787, "module-networkx.algorithms.swap"]], "Threshold Graphs": [[788, "module-networkx.algorithms.threshold"]], "Tournament": [[789, "module-networkx.algorithms.tournament"]], "Traversal": [[790, "traversal"]], "Depth First Search": [[790, "module-networkx.algorithms.traversal.depth_first_search"]], "Breadth First Search": [[790, "module-networkx.algorithms.traversal.breadth_first_search"]], "Beam search": [[790, "module-networkx.algorithms.traversal.beamsearch"]], "Depth First Search on Edges": [[790, "module-networkx.algorithms.traversal.edgedfs"]], "Breadth First Search on Edges": [[790, "module-networkx.algorithms.traversal.edgebfs"]], "Tree": [[791, "tree"]], "Recognition": [[791, "module-networkx.algorithms.tree.recognition"]], "Recognition Tests": [[791, "recognition-tests"]], "Branchings and Spanning Arborescences": [[791, "module-networkx.algorithms.tree.branchings"]], "Encoding and decoding": [[791, "module-networkx.algorithms.tree.coding"]], "Operations": [[791, "module-networkx.algorithms.tree.operations"]], "Spanning Trees": [[791, "module-networkx.algorithms.tree.mst"]], "Exceptions": [[791, "exceptions"], [1043, "module-networkx.exception"]], "Vitality": [[793, "module-networkx.algorithms.vitality"]], "Voronoi cells": [[794, "module-networkx.algorithms.voronoi"]], "Wiener index": [[795, "module-networkx.algorithms.wiener"]], "DiGraph\u2014Directed graphs with self loops": [[796, "digraph-directed-graphs-with-self-loops"]], "Overview": [[796, "overview"], [1037, "overview"], [1039, "overview"], [1040, "overview"]], "Methods": [[796, "methods"], [1037, "methods"], [1039, "methods"], [1040, "methods"]], "Adding and removing nodes and edges": [[796, 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"FilterMultiInner.get": [[819, "filtermultiinner-get"]], "FilterMultiInner.items": [[820, "filtermultiinner-items"]], "FilterMultiInner.keys": [[821, "filtermultiinner-keys"]], "FilterMultiInner.values": [[822, "filtermultiinner-values"]], "MultiAdjacencyView.copy": [[823, "multiadjacencyview-copy"]], "MultiAdjacencyView.get": [[824, "multiadjacencyview-get"]], "MultiAdjacencyView.items": [[825, "multiadjacencyview-items"]], "MultiAdjacencyView.keys": [[826, "multiadjacencyview-keys"]], "MultiAdjacencyView.values": [[827, "multiadjacencyview-values"]], "UnionAdjacency.copy": [[828, "unionadjacency-copy"]], "UnionAdjacency.get": [[829, "unionadjacency-get"]], "UnionAdjacency.items": [[830, "unionadjacency-items"]], "UnionAdjacency.keys": [[831, "unionadjacency-keys"]], "UnionAdjacency.values": [[832, "unionadjacency-values"]], "UnionAtlas.copy": [[833, "unionatlas-copy"]], "UnionAtlas.get": [[834, "unionatlas-get"]], "UnionAtlas.items": [[835, "unionatlas-items"]], "UnionAtlas.keys": 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"DiGraph.nodes": [[873, "digraph-nodes"]], "DiGraph.number_of_edges": [[874, "digraph-number-of-edges"]], "DiGraph.number_of_nodes": [[875, "digraph-number-of-nodes"]], "DiGraph.order": [[876, "digraph-order"]], "DiGraph.out_degree": [[877, "digraph-out-degree"]], "DiGraph.out_edges": [[878, "digraph-out-edges"]], "DiGraph.pred": [[879, "digraph-pred"]], "DiGraph.predecessors": [[880, "digraph-predecessors"]], "DiGraph.remove_edge": [[881, "digraph-remove-edge"]], "DiGraph.remove_edges_from": [[882, "digraph-remove-edges-from"]], "DiGraph.remove_node": [[883, "digraph-remove-node"]], "DiGraph.remove_nodes_from": [[884, "digraph-remove-nodes-from"]], "DiGraph.reverse": [[885, "digraph-reverse"]], "DiGraph.size": [[886, "digraph-size"]], "DiGraph.subgraph": [[887, "digraph-subgraph"]], "DiGraph.succ": [[888, "digraph-succ"]], "DiGraph.successors": [[889, "digraph-successors"]], "DiGraph.to_directed": [[890, "digraph-to-directed"]], "DiGraph.to_undirected": [[891, 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"MultiDiGraph.__iter__": [[932, "multidigraph-iter"]], "MultiDiGraph.__len__": [[933, "multidigraph-len"]], "MultiDiGraph.add_edge": [[934, "multidigraph-add-edge"]], "MultiDiGraph.add_edges_from": [[935, "multidigraph-add-edges-from"]], "MultiDiGraph.add_node": [[936, "multidigraph-add-node"]], "MultiDiGraph.add_nodes_from": [[937, "multidigraph-add-nodes-from"]], "MultiDiGraph.add_weighted_edges_from": [[938, "multidigraph-add-weighted-edges-from"]], "MultiDiGraph.adj": [[939, "multidigraph-adj"]], "MultiDiGraph.adjacency": [[940, "multidigraph-adjacency"]], "MultiDiGraph.clear": [[941, "multidigraph-clear"]], "MultiDiGraph.clear_edges": [[942, "multidigraph-clear-edges"]], "MultiDiGraph.copy": [[943, "multidigraph-copy"]], "MultiDiGraph.degree": [[944, "multidigraph-degree"]], "MultiDiGraph.edge_subgraph": [[945, "multidigraph-edge-subgraph"]], "MultiDiGraph.edges": [[946, "multidigraph-edges"]], "MultiDiGraph.get_edge_data": [[947, "multidigraph-get-edge-data"]], 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Applying classic graph operations, such as:": [[1426, "applying-classic-graph-operations-such-as"]], "2. Using a call to one of the classic small graphs, e.g.,": [[1426, "using-a-call-to-one-of-the-classic-small-graphs-e-g"]], "3. Using a (constructive) generator for a classic graph, e.g.,": [[1426, "using-a-constructive-generator-for-a-classic-graph-e-g"]], "4. Using a stochastic graph generator, e.g,": [[1426, "using-a-stochastic-graph-generator-e-g"]], "5. Reading a graph stored in a file using common graph formats": [[1426, "reading-a-graph-stored-in-a-file-using-common-graph-formats"]], "Analyzing graphs": [[1426, "analyzing-graphs"]], "Drawing graphs": [[1426, "drawing-graphs"]]}, "indexentries": {"module": [[112, "module-networkx.algorithms.approximation"], [112, "module-networkx.algorithms.approximation.clique"], [112, "module-networkx.algorithms.approximation.clustering_coefficient"], [112, "module-networkx.algorithms.approximation.connectivity"], [112, "module-networkx.algorithms.approximation.distance_measures"], [112, "module-networkx.algorithms.approximation.dominating_set"], [112, "module-networkx.algorithms.approximation.kcomponents"], [112, "module-networkx.algorithms.approximation.matching"], [112, "module-networkx.algorithms.approximation.maxcut"], [112, "module-networkx.algorithms.approximation.ramsey"], [112, "module-networkx.algorithms.approximation.steinertree"], [112, 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"networkx.algorithms.cuts": [[130, "module-networkx.algorithms.cuts"]], "networkx.algorithms.cycles": [[131, "module-networkx.algorithms.cycles"]], "networkx.algorithms.d_separation": [[132, "module-networkx.algorithms.d_separation"]], "networkx.algorithms.dag": [[133, "module-networkx.algorithms.dag"]], "networkx.algorithms.distance_measures": [[134, "module-networkx.algorithms.distance_measures"]], "networkx.algorithms.distance_regular": [[135, "module-networkx.algorithms.distance_regular"]], "networkx.algorithms.dominance": [[136, "module-networkx.algorithms.dominance"]], "networkx.algorithms.dominating": [[137, "module-networkx.algorithms.dominating"]], "networkx.algorithms.efficiency_measures": [[138, "module-networkx.algorithms.efficiency_measures"]], "networkx.algorithms.euler": [[139, "module-networkx.algorithms.euler"]], "networkx.algorithms.flow": [[140, "module-networkx.algorithms.flow"]], "construct() (edgecomponentauxgraph class method)": [[141, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.construct"]], "k_edge_components() (edgecomponentauxgraph method)": [[142, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.k_edge_components"]], "k_edge_subgraphs() (edgecomponentauxgraph method)": [[143, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.k_edge_subgraphs"]], "analyze_symmetry() (ismags method)": [[144, "networkx.algorithms.isomorphism.ISMAGS.analyze_symmetry"]], "find_isomorphisms() (ismags method)": [[145, "networkx.algorithms.isomorphism.ISMAGS.find_isomorphisms"]], "is_isomorphic() (ismags method)": [[146, "networkx.algorithms.isomorphism.ISMAGS.is_isomorphic"]], "isomorphisms_iter() (ismags method)": [[147, "networkx.algorithms.isomorphism.ISMAGS.isomorphisms_iter"]], "largest_common_subgraph() (ismags method)": [[148, "networkx.algorithms.isomorphism.ISMAGS.largest_common_subgraph"]], "subgraph_is_isomorphic() (ismags method)": [[149, "networkx.algorithms.isomorphism.ISMAGS.subgraph_is_isomorphic"]], "subgraph_isomorphisms_iter() (ismags method)": [[150, "networkx.algorithms.isomorphism.ISMAGS.subgraph_isomorphisms_iter"]], "add_edge() (planarembedding method)": [[151, "networkx.algorithms.planarity.PlanarEmbedding.add_edge"]], "add_edges_from() (planarembedding method)": [[152, "networkx.algorithms.planarity.PlanarEmbedding.add_edges_from"]], "add_half_edge_ccw() (planarembedding method)": [[153, "networkx.algorithms.planarity.PlanarEmbedding.add_half_edge_ccw"]], "add_half_edge_cw() (planarembedding method)": [[154, "networkx.algorithms.planarity.PlanarEmbedding.add_half_edge_cw"]], "add_half_edge_first() (planarembedding method)": [[155, "networkx.algorithms.planarity.PlanarEmbedding.add_half_edge_first"]], "add_node() (planarembedding method)": [[156, "networkx.algorithms.planarity.PlanarEmbedding.add_node"]], "add_nodes_from() (planarembedding method)": [[157, "networkx.algorithms.planarity.PlanarEmbedding.add_nodes_from"]], "add_weighted_edges_from() (planarembedding method)": [[158, "networkx.algorithms.planarity.PlanarEmbedding.add_weighted_edges_from"]], "adj (planarembedding property)": [[159, "networkx.algorithms.planarity.PlanarEmbedding.adj"]], "adjacency() (planarembedding method)": [[160, "networkx.algorithms.planarity.PlanarEmbedding.adjacency"]], "check_structure() (planarembedding method)": [[161, "networkx.algorithms.planarity.PlanarEmbedding.check_structure"]], "clear() (planarembedding method)": [[162, "networkx.algorithms.planarity.PlanarEmbedding.clear"]], "clear_edges() (planarembedding method)": [[163, "networkx.algorithms.planarity.PlanarEmbedding.clear_edges"]], "connect_components() (planarembedding method)": [[164, "networkx.algorithms.planarity.PlanarEmbedding.connect_components"]], "copy() (planarembedding method)": [[165, "networkx.algorithms.planarity.PlanarEmbedding.copy"]], "degree (planarembedding property)": 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"networkx.algorithms.planarity.PlanarEmbedding.in_degree"]], "in_edges (planarembedding property)": [[176, "networkx.algorithms.planarity.PlanarEmbedding.in_edges"]], "is_directed() (planarembedding method)": [[177, "networkx.algorithms.planarity.PlanarEmbedding.is_directed"]], "is_multigraph() (planarembedding method)": [[178, "networkx.algorithms.planarity.PlanarEmbedding.is_multigraph"]], "name (planarembedding property)": [[179, "networkx.algorithms.planarity.PlanarEmbedding.name"]], "nbunch_iter() (planarembedding method)": [[180, "networkx.algorithms.planarity.PlanarEmbedding.nbunch_iter"]], "neighbors() (planarembedding method)": [[181, "networkx.algorithms.planarity.PlanarEmbedding.neighbors"]], "neighbors_cw_order() (planarembedding method)": [[182, "networkx.algorithms.planarity.PlanarEmbedding.neighbors_cw_order"]], "next_face_half_edge() (planarembedding method)": [[183, "networkx.algorithms.planarity.PlanarEmbedding.next_face_half_edge"]], "nodes (planarembedding property)": [[184, "networkx.algorithms.planarity.PlanarEmbedding.nodes"]], "number_of_edges() (planarembedding method)": [[185, "networkx.algorithms.planarity.PlanarEmbedding.number_of_edges"]], "number_of_nodes() (planarembedding method)": [[186, "networkx.algorithms.planarity.PlanarEmbedding.number_of_nodes"]], "order() (planarembedding method)": [[187, "networkx.algorithms.planarity.PlanarEmbedding.order"]], "out_degree (planarembedding property)": [[188, "networkx.algorithms.planarity.PlanarEmbedding.out_degree"]], "out_edges (planarembedding property)": [[189, "networkx.algorithms.planarity.PlanarEmbedding.out_edges"]], "pred (planarembedding property)": [[190, "networkx.algorithms.planarity.PlanarEmbedding.pred"]], "predecessors() (planarembedding method)": [[191, "networkx.algorithms.planarity.PlanarEmbedding.predecessors"]], "remove_edge() (planarembedding method)": [[192, "networkx.algorithms.planarity.PlanarEmbedding.remove_edge"]], "remove_edges_from() (planarembedding method)": [[193, "networkx.algorithms.planarity.PlanarEmbedding.remove_edges_from"]], "remove_node() (planarembedding method)": [[194, "networkx.algorithms.planarity.PlanarEmbedding.remove_node"]], "remove_nodes_from() (planarembedding method)": [[195, "networkx.algorithms.planarity.PlanarEmbedding.remove_nodes_from"]], "reverse() (planarembedding method)": [[196, "networkx.algorithms.planarity.PlanarEmbedding.reverse"]], "set_data() (planarembedding method)": [[197, "networkx.algorithms.planarity.PlanarEmbedding.set_data"]], "size() (planarembedding method)": [[198, "networkx.algorithms.planarity.PlanarEmbedding.size"]], "subgraph() (planarembedding method)": [[199, "networkx.algorithms.planarity.PlanarEmbedding.subgraph"]], "succ (planarembedding property)": [[200, "networkx.algorithms.planarity.PlanarEmbedding.succ"]], "successors() (planarembedding method)": [[201, "networkx.algorithms.planarity.PlanarEmbedding.successors"]], "to_directed() (planarembedding method)": [[202, "networkx.algorithms.planarity.PlanarEmbedding.to_directed"]], "to_directed_class() (planarembedding method)": [[203, "networkx.algorithms.planarity.PlanarEmbedding.to_directed_class"]], "to_undirected() (planarembedding method)": [[204, "networkx.algorithms.planarity.PlanarEmbedding.to_undirected"]], "to_undirected_class() (planarembedding method)": [[205, "networkx.algorithms.planarity.PlanarEmbedding.to_undirected_class"]], "traverse_face() (planarembedding method)": [[206, "networkx.algorithms.planarity.PlanarEmbedding.traverse_face"]], "update() (planarembedding method)": [[207, "networkx.algorithms.planarity.PlanarEmbedding.update"]], "find_optimum() (edmonds method)": [[208, "networkx.algorithms.tree.branchings.Edmonds.find_optimum"]], "clique_removal() (in module networkx.algorithms.approximation.clique)": [[209, "networkx.algorithms.approximation.clique.clique_removal"]], "large_clique_size() (in module networkx.algorithms.approximation.clique)": [[210, "networkx.algorithms.approximation.clique.large_clique_size"]], "max_clique() (in module networkx.algorithms.approximation.clique)": [[211, "networkx.algorithms.approximation.clique.max_clique"]], "maximum_independent_set() (in module networkx.algorithms.approximation.clique)": [[212, "networkx.algorithms.approximation.clique.maximum_independent_set"]], "average_clustering() (in module networkx.algorithms.approximation.clustering_coefficient)": [[213, "networkx.algorithms.approximation.clustering_coefficient.average_clustering"]], "all_pairs_node_connectivity() (in module networkx.algorithms.approximation.connectivity)": [[214, "networkx.algorithms.approximation.connectivity.all_pairs_node_connectivity"]], "local_node_connectivity() (in module networkx.algorithms.approximation.connectivity)": [[215, "networkx.algorithms.approximation.connectivity.local_node_connectivity"]], "node_connectivity() (in module networkx.algorithms.approximation.connectivity)": [[216, "networkx.algorithms.approximation.connectivity.node_connectivity"]], "diameter() (in module networkx.algorithms.approximation.distance_measures)": [[217, "networkx.algorithms.approximation.distance_measures.diameter"]], "min_edge_dominating_set() (in module networkx.algorithms.approximation.dominating_set)": [[218, "networkx.algorithms.approximation.dominating_set.min_edge_dominating_set"]], "min_weighted_dominating_set() (in module networkx.algorithms.approximation.dominating_set)": [[219, "networkx.algorithms.approximation.dominating_set.min_weighted_dominating_set"]], "k_components() (in module networkx.algorithms.approximation.kcomponents)": [[220, "networkx.algorithms.approximation.kcomponents.k_components"]], "min_maximal_matching() (in module networkx.algorithms.approximation.matching)": [[221, "networkx.algorithms.approximation.matching.min_maximal_matching"]], "one_exchange() (in module networkx.algorithms.approximation.maxcut)": [[222, "networkx.algorithms.approximation.maxcut.one_exchange"]], "randomized_partitioning() (in module networkx.algorithms.approximation.maxcut)": [[223, "networkx.algorithms.approximation.maxcut.randomized_partitioning"]], "ramsey_r2() (in module networkx.algorithms.approximation.ramsey)": [[224, "networkx.algorithms.approximation.ramsey.ramsey_R2"]], "metric_closure() (in module networkx.algorithms.approximation.steinertree)": [[225, "networkx.algorithms.approximation.steinertree.metric_closure"]], "steiner_tree() (in module networkx.algorithms.approximation.steinertree)": [[226, "networkx.algorithms.approximation.steinertree.steiner_tree"]], "asadpour_atsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[227, "networkx.algorithms.approximation.traveling_salesman.asadpour_atsp"]], "christofides() (in module networkx.algorithms.approximation.traveling_salesman)": [[228, "networkx.algorithms.approximation.traveling_salesman.christofides"]], "greedy_tsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[229, "networkx.algorithms.approximation.traveling_salesman.greedy_tsp"]], "simulated_annealing_tsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[230, "networkx.algorithms.approximation.traveling_salesman.simulated_annealing_tsp"]], "threshold_accepting_tsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[231, "networkx.algorithms.approximation.traveling_salesman.threshold_accepting_tsp"]], "traveling_salesman_problem() (in module networkx.algorithms.approximation.traveling_salesman)": [[232, "networkx.algorithms.approximation.traveling_salesman.traveling_salesman_problem"]], "treewidth_min_degree() (in module networkx.algorithms.approximation.treewidth)": [[233, "networkx.algorithms.approximation.treewidth.treewidth_min_degree"]], "treewidth_min_fill_in() (in module networkx.algorithms.approximation.treewidth)": [[234, "networkx.algorithms.approximation.treewidth.treewidth_min_fill_in"]], "min_weighted_vertex_cover() (in module networkx.algorithms.approximation.vertex_cover)": [[235, "networkx.algorithms.approximation.vertex_cover.min_weighted_vertex_cover"]], "attribute_assortativity_coefficient() (in module networkx.algorithms.assortativity)": [[236, "networkx.algorithms.assortativity.attribute_assortativity_coefficient"]], "attribute_mixing_dict() (in module networkx.algorithms.assortativity)": [[237, "networkx.algorithms.assortativity.attribute_mixing_dict"]], "attribute_mixing_matrix() (in module networkx.algorithms.assortativity)": [[238, "networkx.algorithms.assortativity.attribute_mixing_matrix"]], "average_degree_connectivity() (in module networkx.algorithms.assortativity)": [[239, "networkx.algorithms.assortativity.average_degree_connectivity"]], "average_neighbor_degree() (in module networkx.algorithms.assortativity)": [[240, "networkx.algorithms.assortativity.average_neighbor_degree"]], "degree_assortativity_coefficient() (in module networkx.algorithms.assortativity)": [[241, "networkx.algorithms.assortativity.degree_assortativity_coefficient"]], "degree_mixing_dict() (in module networkx.algorithms.assortativity)": [[242, "networkx.algorithms.assortativity.degree_mixing_dict"]], "degree_mixing_matrix() (in module networkx.algorithms.assortativity)": [[243, "networkx.algorithms.assortativity.degree_mixing_matrix"]], "degree_pearson_correlation_coefficient() (in module networkx.algorithms.assortativity)": [[244, "networkx.algorithms.assortativity.degree_pearson_correlation_coefficient"]], "mixing_dict() (in module networkx.algorithms.assortativity)": [[245, "networkx.algorithms.assortativity.mixing_dict"]], "node_attribute_xy() (in module networkx.algorithms.assortativity)": [[246, "networkx.algorithms.assortativity.node_attribute_xy"]], "node_degree_xy() (in module networkx.algorithms.assortativity)": [[247, "networkx.algorithms.assortativity.node_degree_xy"]], "numeric_assortativity_coefficient() (in module networkx.algorithms.assortativity)": [[248, "networkx.algorithms.assortativity.numeric_assortativity_coefficient"]], "find_asteroidal_triple() (in module networkx.algorithms.asteroidal)": [[249, "networkx.algorithms.asteroidal.find_asteroidal_triple"]], "is_at_free() (in module networkx.algorithms.asteroidal)": [[250, "networkx.algorithms.asteroidal.is_at_free"]], "color() (in module networkx.algorithms.bipartite.basic)": [[251, "networkx.algorithms.bipartite.basic.color"]], "degrees() (in module networkx.algorithms.bipartite.basic)": [[252, "networkx.algorithms.bipartite.basic.degrees"]], "density() (in module networkx.algorithms.bipartite.basic)": [[253, "networkx.algorithms.bipartite.basic.density"]], "is_bipartite() (in module networkx.algorithms.bipartite.basic)": [[254, "networkx.algorithms.bipartite.basic.is_bipartite"]], "is_bipartite_node_set() (in module networkx.algorithms.bipartite.basic)": [[255, "networkx.algorithms.bipartite.basic.is_bipartite_node_set"]], "sets() (in module networkx.algorithms.bipartite.basic)": [[256, "networkx.algorithms.bipartite.basic.sets"]], "betweenness_centrality() (in module networkx.algorithms.bipartite.centrality)": [[257, "networkx.algorithms.bipartite.centrality.betweenness_centrality"]], "closeness_centrality() (in module networkx.algorithms.bipartite.centrality)": [[258, "networkx.algorithms.bipartite.centrality.closeness_centrality"]], "degree_centrality() (in module networkx.algorithms.bipartite.centrality)": [[259, "networkx.algorithms.bipartite.centrality.degree_centrality"]], "average_clustering() (in module networkx.algorithms.bipartite.cluster)": [[260, "networkx.algorithms.bipartite.cluster.average_clustering"]], "clustering() (in module networkx.algorithms.bipartite.cluster)": [[261, "networkx.algorithms.bipartite.cluster.clustering"]], "latapy_clustering() (in module networkx.algorithms.bipartite.cluster)": [[262, "networkx.algorithms.bipartite.cluster.latapy_clustering"]], "robins_alexander_clustering() (in module networkx.algorithms.bipartite.cluster)": [[263, "networkx.algorithms.bipartite.cluster.robins_alexander_clustering"]], "min_edge_cover() (in module networkx.algorithms.bipartite.covering)": [[264, "networkx.algorithms.bipartite.covering.min_edge_cover"]], "generate_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[265, "networkx.algorithms.bipartite.edgelist.generate_edgelist"]], "parse_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[266, "networkx.algorithms.bipartite.edgelist.parse_edgelist"]], "read_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[267, "networkx.algorithms.bipartite.edgelist.read_edgelist"]], "write_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[268, "networkx.algorithms.bipartite.edgelist.write_edgelist"]], "alternating_havel_hakimi_graph() (in module networkx.algorithms.bipartite.generators)": [[269, "networkx.algorithms.bipartite.generators.alternating_havel_hakimi_graph"]], "complete_bipartite_graph() (in module networkx.algorithms.bipartite.generators)": [[270, "networkx.algorithms.bipartite.generators.complete_bipartite_graph"]], "configuration_model() (in module networkx.algorithms.bipartite.generators)": [[271, "networkx.algorithms.bipartite.generators.configuration_model"]], "gnmk_random_graph() (in module networkx.algorithms.bipartite.generators)": [[272, "networkx.algorithms.bipartite.generators.gnmk_random_graph"]], "havel_hakimi_graph() (in module networkx.algorithms.bipartite.generators)": [[273, "networkx.algorithms.bipartite.generators.havel_hakimi_graph"]], "preferential_attachment_graph() (in module networkx.algorithms.bipartite.generators)": [[274, "networkx.algorithms.bipartite.generators.preferential_attachment_graph"]], "random_graph() (in module networkx.algorithms.bipartite.generators)": [[275, "networkx.algorithms.bipartite.generators.random_graph"]], "reverse_havel_hakimi_graph() (in module networkx.algorithms.bipartite.generators)": [[276, "networkx.algorithms.bipartite.generators.reverse_havel_hakimi_graph"]], "eppstein_matching() (in module networkx.algorithms.bipartite.matching)": [[277, "networkx.algorithms.bipartite.matching.eppstein_matching"]], "hopcroft_karp_matching() (in module networkx.algorithms.bipartite.matching)": [[278, "networkx.algorithms.bipartite.matching.hopcroft_karp_matching"]], "maximum_matching() (in module networkx.algorithms.bipartite.matching)": [[279, "networkx.algorithms.bipartite.matching.maximum_matching"]], "minimum_weight_full_matching() (in module networkx.algorithms.bipartite.matching)": [[280, "networkx.algorithms.bipartite.matching.minimum_weight_full_matching"]], "to_vertex_cover() (in module networkx.algorithms.bipartite.matching)": [[281, "networkx.algorithms.bipartite.matching.to_vertex_cover"]], "biadjacency_matrix() (in module networkx.algorithms.bipartite.matrix)": [[282, "networkx.algorithms.bipartite.matrix.biadjacency_matrix"]], "from_biadjacency_matrix() (in module networkx.algorithms.bipartite.matrix)": [[283, "networkx.algorithms.bipartite.matrix.from_biadjacency_matrix"]], "collaboration_weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[284, "networkx.algorithms.bipartite.projection.collaboration_weighted_projected_graph"]], "generic_weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[285, "networkx.algorithms.bipartite.projection.generic_weighted_projected_graph"]], "overlap_weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[286, "networkx.algorithms.bipartite.projection.overlap_weighted_projected_graph"]], "projected_graph() (in module networkx.algorithms.bipartite.projection)": [[287, "networkx.algorithms.bipartite.projection.projected_graph"]], "weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[288, "networkx.algorithms.bipartite.projection.weighted_projected_graph"]], "node_redundancy() (in module networkx.algorithms.bipartite.redundancy)": [[289, "networkx.algorithms.bipartite.redundancy.node_redundancy"]], "spectral_bipartivity() (in module networkx.algorithms.bipartite.spectral)": [[290, "networkx.algorithms.bipartite.spectral.spectral_bipartivity"]], "edge_boundary() (in module networkx.algorithms.boundary)": [[291, "networkx.algorithms.boundary.edge_boundary"]], "node_boundary() (in module networkx.algorithms.boundary)": [[292, "networkx.algorithms.boundary.node_boundary"]], "bridges() (in module networkx.algorithms.bridges)": [[293, "networkx.algorithms.bridges.bridges"]], "has_bridges() (in module networkx.algorithms.bridges)": [[294, "networkx.algorithms.bridges.has_bridges"]], "local_bridges() (in module networkx.algorithms.bridges)": [[295, "networkx.algorithms.bridges.local_bridges"]], "approximate_current_flow_betweenness_centrality() (in module networkx.algorithms.centrality)": [[296, "networkx.algorithms.centrality.approximate_current_flow_betweenness_centrality"]], "betweenness_centrality() (in module networkx.algorithms.centrality)": [[297, "networkx.algorithms.centrality.betweenness_centrality"]], "betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[298, "networkx.algorithms.centrality.betweenness_centrality_subset"]], "closeness_centrality() (in module networkx.algorithms.centrality)": [[299, "networkx.algorithms.centrality.closeness_centrality"]], "communicability_betweenness_centrality() (in module networkx.algorithms.centrality)": [[300, "networkx.algorithms.centrality.communicability_betweenness_centrality"]], "current_flow_betweenness_centrality() (in module networkx.algorithms.centrality)": [[301, "networkx.algorithms.centrality.current_flow_betweenness_centrality"]], "current_flow_betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[302, "networkx.algorithms.centrality.current_flow_betweenness_centrality_subset"]], "current_flow_closeness_centrality() (in module networkx.algorithms.centrality)": [[303, "networkx.algorithms.centrality.current_flow_closeness_centrality"]], "degree_centrality() (in module networkx.algorithms.centrality)": [[304, "networkx.algorithms.centrality.degree_centrality"]], "dispersion() (in module networkx.algorithms.centrality)": [[305, "networkx.algorithms.centrality.dispersion"]], "edge_betweenness_centrality() (in module networkx.algorithms.centrality)": [[306, "networkx.algorithms.centrality.edge_betweenness_centrality"]], "edge_betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[307, "networkx.algorithms.centrality.edge_betweenness_centrality_subset"]], "edge_current_flow_betweenness_centrality() (in module networkx.algorithms.centrality)": [[308, "networkx.algorithms.centrality.edge_current_flow_betweenness_centrality"]], "edge_current_flow_betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[309, "networkx.algorithms.centrality.edge_current_flow_betweenness_centrality_subset"]], "edge_load_centrality() (in module networkx.algorithms.centrality)": [[310, "networkx.algorithms.centrality.edge_load_centrality"]], "eigenvector_centrality() (in module networkx.algorithms.centrality)": [[311, "networkx.algorithms.centrality.eigenvector_centrality"]], "eigenvector_centrality_numpy() (in module networkx.algorithms.centrality)": [[312, "networkx.algorithms.centrality.eigenvector_centrality_numpy"]], "estrada_index() (in module networkx.algorithms.centrality)": [[313, "networkx.algorithms.centrality.estrada_index"]], "global_reaching_centrality() (in module networkx.algorithms.centrality)": [[314, "networkx.algorithms.centrality.global_reaching_centrality"]], "group_betweenness_centrality() (in module networkx.algorithms.centrality)": [[315, "networkx.algorithms.centrality.group_betweenness_centrality"]], "group_closeness_centrality() (in module networkx.algorithms.centrality)": [[316, "networkx.algorithms.centrality.group_closeness_centrality"]], "group_degree_centrality() (in module networkx.algorithms.centrality)": [[317, "networkx.algorithms.centrality.group_degree_centrality"]], "group_in_degree_centrality() (in module networkx.algorithms.centrality)": [[318, "networkx.algorithms.centrality.group_in_degree_centrality"]], "group_out_degree_centrality() (in module networkx.algorithms.centrality)": [[319, "networkx.algorithms.centrality.group_out_degree_centrality"]], "harmonic_centrality() (in module networkx.algorithms.centrality)": [[320, "networkx.algorithms.centrality.harmonic_centrality"]], "in_degree_centrality() (in module networkx.algorithms.centrality)": [[321, "networkx.algorithms.centrality.in_degree_centrality"]], "incremental_closeness_centrality() (in module networkx.algorithms.centrality)": [[322, "networkx.algorithms.centrality.incremental_closeness_centrality"]], "information_centrality() (in module networkx.algorithms.centrality)": [[323, "networkx.algorithms.centrality.information_centrality"]], "katz_centrality() (in module networkx.algorithms.centrality)": [[324, "networkx.algorithms.centrality.katz_centrality"]], "katz_centrality_numpy() (in module networkx.algorithms.centrality)": [[325, "networkx.algorithms.centrality.katz_centrality_numpy"]], "load_centrality() (in module networkx.algorithms.centrality)": [[326, "networkx.algorithms.centrality.load_centrality"]], "local_reaching_centrality() (in module networkx.algorithms.centrality)": [[327, "networkx.algorithms.centrality.local_reaching_centrality"]], "out_degree_centrality() (in module networkx.algorithms.centrality)": [[328, "networkx.algorithms.centrality.out_degree_centrality"]], "percolation_centrality() (in module networkx.algorithms.centrality)": [[329, "networkx.algorithms.centrality.percolation_centrality"]], "prominent_group() (in module networkx.algorithms.centrality)": [[330, "networkx.algorithms.centrality.prominent_group"]], "second_order_centrality() (in module networkx.algorithms.centrality)": [[331, "networkx.algorithms.centrality.second_order_centrality"]], "subgraph_centrality() (in module networkx.algorithms.centrality)": [[332, "networkx.algorithms.centrality.subgraph_centrality"]], "subgraph_centrality_exp() (in module networkx.algorithms.centrality)": [[333, "networkx.algorithms.centrality.subgraph_centrality_exp"]], "trophic_differences() (in module networkx.algorithms.centrality)": [[334, "networkx.algorithms.centrality.trophic_differences"]], "trophic_incoherence_parameter() (in module networkx.algorithms.centrality)": [[335, "networkx.algorithms.centrality.trophic_incoherence_parameter"]], "trophic_levels() (in module networkx.algorithms.centrality)": [[336, "networkx.algorithms.centrality.trophic_levels"]], "voterank() (in module networkx.algorithms.centrality)": [[337, "networkx.algorithms.centrality.voterank"]], "chain_decomposition() (in module networkx.algorithms.chains)": [[338, "networkx.algorithms.chains.chain_decomposition"]], "chordal_graph_cliques() (in module networkx.algorithms.chordal)": [[339, "networkx.algorithms.chordal.chordal_graph_cliques"]], "chordal_graph_treewidth() (in module networkx.algorithms.chordal)": [[340, "networkx.algorithms.chordal.chordal_graph_treewidth"]], "complete_to_chordal_graph() (in module networkx.algorithms.chordal)": [[341, "networkx.algorithms.chordal.complete_to_chordal_graph"]], "find_induced_nodes() (in module networkx.algorithms.chordal)": [[342, "networkx.algorithms.chordal.find_induced_nodes"]], "is_chordal() (in module networkx.algorithms.chordal)": [[343, "networkx.algorithms.chordal.is_chordal"]], "cliques_containing_node() (in module networkx.algorithms.clique)": [[344, "networkx.algorithms.clique.cliques_containing_node"]], "enumerate_all_cliques() (in module networkx.algorithms.clique)": [[345, "networkx.algorithms.clique.enumerate_all_cliques"]], "find_cliques() (in module networkx.algorithms.clique)": [[346, "networkx.algorithms.clique.find_cliques"]], "find_cliques_recursive() (in module networkx.algorithms.clique)": [[347, "networkx.algorithms.clique.find_cliques_recursive"]], "graph_clique_number() (in module networkx.algorithms.clique)": [[348, "networkx.algorithms.clique.graph_clique_number"]], "graph_number_of_cliques() (in module networkx.algorithms.clique)": [[349, "networkx.algorithms.clique.graph_number_of_cliques"]], "make_clique_bipartite() (in module networkx.algorithms.clique)": [[350, "networkx.algorithms.clique.make_clique_bipartite"]], "make_max_clique_graph() (in module networkx.algorithms.clique)": [[351, "networkx.algorithms.clique.make_max_clique_graph"]], "max_weight_clique() (in module networkx.algorithms.clique)": [[352, "networkx.algorithms.clique.max_weight_clique"]], "node_clique_number() (in module networkx.algorithms.clique)": [[353, "networkx.algorithms.clique.node_clique_number"]], "number_of_cliques() (in module networkx.algorithms.clique)": [[354, "networkx.algorithms.clique.number_of_cliques"]], "average_clustering() (in module networkx.algorithms.cluster)": [[355, "networkx.algorithms.cluster.average_clustering"]], "clustering() (in module networkx.algorithms.cluster)": [[356, "networkx.algorithms.cluster.clustering"]], "generalized_degree() (in module networkx.algorithms.cluster)": [[357, "networkx.algorithms.cluster.generalized_degree"]], "square_clustering() (in module networkx.algorithms.cluster)": [[358, "networkx.algorithms.cluster.square_clustering"]], "transitivity() (in module networkx.algorithms.cluster)": [[359, "networkx.algorithms.cluster.transitivity"]], "triangles() (in module networkx.algorithms.cluster)": [[360, "networkx.algorithms.cluster.triangles"]], "equitable_color() (in module networkx.algorithms.coloring)": [[361, "networkx.algorithms.coloring.equitable_color"]], "greedy_color() (in module networkx.algorithms.coloring)": [[362, "networkx.algorithms.coloring.greedy_color"]], "strategy_connected_sequential() (in module networkx.algorithms.coloring)": [[363, "networkx.algorithms.coloring.strategy_connected_sequential"]], "strategy_connected_sequential_bfs() (in module networkx.algorithms.coloring)": [[364, "networkx.algorithms.coloring.strategy_connected_sequential_bfs"]], "strategy_connected_sequential_dfs() (in module networkx.algorithms.coloring)": [[365, "networkx.algorithms.coloring.strategy_connected_sequential_dfs"]], "strategy_independent_set() (in module networkx.algorithms.coloring)": [[366, "networkx.algorithms.coloring.strategy_independent_set"]], "strategy_largest_first() (in module networkx.algorithms.coloring)": [[367, "networkx.algorithms.coloring.strategy_largest_first"]], "strategy_random_sequential() (in module networkx.algorithms.coloring)": [[368, "networkx.algorithms.coloring.strategy_random_sequential"]], "strategy_saturation_largest_first() (in module networkx.algorithms.coloring)": [[369, "networkx.algorithms.coloring.strategy_saturation_largest_first"]], "strategy_smallest_last() (in module networkx.algorithms.coloring)": [[370, "networkx.algorithms.coloring.strategy_smallest_last"]], "communicability() (in module networkx.algorithms.communicability_alg)": [[371, "networkx.algorithms.communicability_alg.communicability"]], "communicability_exp() (in module networkx.algorithms.communicability_alg)": [[372, "networkx.algorithms.communicability_alg.communicability_exp"]], "asyn_fluidc() (in module networkx.algorithms.community.asyn_fluid)": [[373, "networkx.algorithms.community.asyn_fluid.asyn_fluidc"]], "girvan_newman() (in module networkx.algorithms.community.centrality)": [[374, "networkx.algorithms.community.centrality.girvan_newman"]], "is_partition() (in module networkx.algorithms.community.community_utils)": [[375, "networkx.algorithms.community.community_utils.is_partition"]], "k_clique_communities() (in module networkx.algorithms.community.kclique)": [[376, "networkx.algorithms.community.kclique.k_clique_communities"]], "kernighan_lin_bisection() (in module networkx.algorithms.community.kernighan_lin)": [[377, "networkx.algorithms.community.kernighan_lin.kernighan_lin_bisection"]], "asyn_lpa_communities() (in module networkx.algorithms.community.label_propagation)": [[378, "networkx.algorithms.community.label_propagation.asyn_lpa_communities"]], "label_propagation_communities() (in module networkx.algorithms.community.label_propagation)": [[379, "networkx.algorithms.community.label_propagation.label_propagation_communities"]], "louvain_communities() (in module networkx.algorithms.community.louvain)": [[380, "networkx.algorithms.community.louvain.louvain_communities"]], "louvain_partitions() (in module networkx.algorithms.community.louvain)": [[381, "networkx.algorithms.community.louvain.louvain_partitions"]], "lukes_partitioning() (in module networkx.algorithms.community.lukes)": [[382, "networkx.algorithms.community.lukes.lukes_partitioning"]], "greedy_modularity_communities() (in module networkx.algorithms.community.modularity_max)": [[383, "networkx.algorithms.community.modularity_max.greedy_modularity_communities"]], "naive_greedy_modularity_communities() (in module networkx.algorithms.community.modularity_max)": [[384, "networkx.algorithms.community.modularity_max.naive_greedy_modularity_communities"]], "modularity() (in module networkx.algorithms.community.quality)": [[385, "networkx.algorithms.community.quality.modularity"]], "partition_quality() (in module networkx.algorithms.community.quality)": [[386, "networkx.algorithms.community.quality.partition_quality"]], "articulation_points() (in module networkx.algorithms.components)": [[387, "networkx.algorithms.components.articulation_points"]], "attracting_components() (in module networkx.algorithms.components)": [[388, "networkx.algorithms.components.attracting_components"]], "biconnected_component_edges() (in module networkx.algorithms.components)": [[389, "networkx.algorithms.components.biconnected_component_edges"]], "biconnected_components() (in module networkx.algorithms.components)": [[390, "networkx.algorithms.components.biconnected_components"]], "condensation() (in module networkx.algorithms.components)": [[391, "networkx.algorithms.components.condensation"]], "connected_components() (in module networkx.algorithms.components)": [[392, "networkx.algorithms.components.connected_components"]], "is_attracting_component() (in module networkx.algorithms.components)": [[393, "networkx.algorithms.components.is_attracting_component"]], "is_biconnected() (in module networkx.algorithms.components)": [[394, "networkx.algorithms.components.is_biconnected"]], "is_connected() (in module networkx.algorithms.components)": [[395, "networkx.algorithms.components.is_connected"]], "is_semiconnected() (in module networkx.algorithms.components)": [[396, "networkx.algorithms.components.is_semiconnected"]], "is_strongly_connected() (in module networkx.algorithms.components)": [[397, "networkx.algorithms.components.is_strongly_connected"]], "is_weakly_connected() (in module networkx.algorithms.components)": [[398, "networkx.algorithms.components.is_weakly_connected"]], "kosaraju_strongly_connected_components() (in module networkx.algorithms.components)": [[399, "networkx.algorithms.components.kosaraju_strongly_connected_components"]], "node_connected_component() (in module networkx.algorithms.components)": [[400, "networkx.algorithms.components.node_connected_component"]], "number_attracting_components() (in module networkx.algorithms.components)": [[401, "networkx.algorithms.components.number_attracting_components"]], "number_connected_components() (in module networkx.algorithms.components)": [[402, "networkx.algorithms.components.number_connected_components"]], "number_strongly_connected_components() (in module networkx.algorithms.components)": [[403, "networkx.algorithms.components.number_strongly_connected_components"]], "number_weakly_connected_components() (in module networkx.algorithms.components)": [[404, "networkx.algorithms.components.number_weakly_connected_components"]], "strongly_connected_components() (in module networkx.algorithms.components)": [[405, "networkx.algorithms.components.strongly_connected_components"]], "strongly_connected_components_recursive() (in module networkx.algorithms.components)": [[406, "networkx.algorithms.components.strongly_connected_components_recursive"]], "weakly_connected_components() (in module networkx.algorithms.components)": [[407, "networkx.algorithms.components.weakly_connected_components"]], "all_pairs_node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[408, "networkx.algorithms.connectivity.connectivity.all_pairs_node_connectivity"]], "average_node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[409, "networkx.algorithms.connectivity.connectivity.average_node_connectivity"]], "edge_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[410, "networkx.algorithms.connectivity.connectivity.edge_connectivity"]], "local_edge_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[411, "networkx.algorithms.connectivity.connectivity.local_edge_connectivity"]], "local_node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[412, "networkx.algorithms.connectivity.connectivity.local_node_connectivity"]], "node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[413, "networkx.algorithms.connectivity.connectivity.node_connectivity"]], "minimum_edge_cut() (in module networkx.algorithms.connectivity.cuts)": [[414, "networkx.algorithms.connectivity.cuts.minimum_edge_cut"]], "minimum_node_cut() (in module networkx.algorithms.connectivity.cuts)": [[415, "networkx.algorithms.connectivity.cuts.minimum_node_cut"]], "minimum_st_edge_cut() (in module networkx.algorithms.connectivity.cuts)": [[416, "networkx.algorithms.connectivity.cuts.minimum_st_edge_cut"]], "minimum_st_node_cut() (in module networkx.algorithms.connectivity.cuts)": [[417, "networkx.algorithms.connectivity.cuts.minimum_st_node_cut"]], "edge_disjoint_paths() (in module networkx.algorithms.connectivity.disjoint_paths)": [[418, "networkx.algorithms.connectivity.disjoint_paths.edge_disjoint_paths"]], "node_disjoint_paths() (in module networkx.algorithms.connectivity.disjoint_paths)": [[419, "networkx.algorithms.connectivity.disjoint_paths.node_disjoint_paths"]], "is_k_edge_connected() (in module networkx.algorithms.connectivity.edge_augmentation)": [[420, "networkx.algorithms.connectivity.edge_augmentation.is_k_edge_connected"]], "is_locally_k_edge_connected() (in module networkx.algorithms.connectivity.edge_augmentation)": [[421, "networkx.algorithms.connectivity.edge_augmentation.is_locally_k_edge_connected"]], "k_edge_augmentation() (in module networkx.algorithms.connectivity.edge_augmentation)": [[422, "networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation"]], "edgecomponentauxgraph (class in networkx.algorithms.connectivity.edge_kcomponents)": [[423, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph"]], "__init__() (edgecomponentauxgraph method)": [[423, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.__init__"]], "bridge_components() (in module networkx.algorithms.connectivity.edge_kcomponents)": [[424, "networkx.algorithms.connectivity.edge_kcomponents.bridge_components"]], "k_edge_components() (in module networkx.algorithms.connectivity.edge_kcomponents)": [[425, "networkx.algorithms.connectivity.edge_kcomponents.k_edge_components"]], "k_edge_subgraphs() (in module networkx.algorithms.connectivity.edge_kcomponents)": [[426, "networkx.algorithms.connectivity.edge_kcomponents.k_edge_subgraphs"]], "k_components() (in module networkx.algorithms.connectivity.kcomponents)": [[427, "networkx.algorithms.connectivity.kcomponents.k_components"]], "all_node_cuts() (in module networkx.algorithms.connectivity.kcutsets)": [[428, "networkx.algorithms.connectivity.kcutsets.all_node_cuts"]], "stoer_wagner() (in module networkx.algorithms.connectivity.stoerwagner)": [[429, "networkx.algorithms.connectivity.stoerwagner.stoer_wagner"]], "build_auxiliary_edge_connectivity() (in module networkx.algorithms.connectivity.utils)": [[430, "networkx.algorithms.connectivity.utils.build_auxiliary_edge_connectivity"]], "build_auxiliary_node_connectivity() (in module networkx.algorithms.connectivity.utils)": [[431, "networkx.algorithms.connectivity.utils.build_auxiliary_node_connectivity"]], "core_number() (in module networkx.algorithms.core)": [[432, "networkx.algorithms.core.core_number"]], "k_core() (in module networkx.algorithms.core)": [[433, "networkx.algorithms.core.k_core"]], "k_corona() (in module networkx.algorithms.core)": [[434, "networkx.algorithms.core.k_corona"]], "k_crust() (in module networkx.algorithms.core)": [[435, "networkx.algorithms.core.k_crust"]], "k_shell() (in module networkx.algorithms.core)": [[436, "networkx.algorithms.core.k_shell"]], "k_truss() (in module networkx.algorithms.core)": [[437, "networkx.algorithms.core.k_truss"]], "onion_layers() (in module networkx.algorithms.core)": [[438, "networkx.algorithms.core.onion_layers"]], "is_edge_cover() (in module networkx.algorithms.covering)": [[439, "networkx.algorithms.covering.is_edge_cover"]], "min_edge_cover() (in module networkx.algorithms.covering)": [[440, "networkx.algorithms.covering.min_edge_cover"]], "boundary_expansion() (in module networkx.algorithms.cuts)": [[441, "networkx.algorithms.cuts.boundary_expansion"]], "conductance() (in module networkx.algorithms.cuts)": [[442, "networkx.algorithms.cuts.conductance"]], "cut_size() (in module networkx.algorithms.cuts)": [[443, "networkx.algorithms.cuts.cut_size"]], "edge_expansion() (in module networkx.algorithms.cuts)": [[444, "networkx.algorithms.cuts.edge_expansion"]], "mixing_expansion() (in module networkx.algorithms.cuts)": [[445, "networkx.algorithms.cuts.mixing_expansion"]], "node_expansion() (in module networkx.algorithms.cuts)": [[446, "networkx.algorithms.cuts.node_expansion"]], "normalized_cut_size() (in module networkx.algorithms.cuts)": [[447, "networkx.algorithms.cuts.normalized_cut_size"]], "volume() (in module networkx.algorithms.cuts)": [[448, "networkx.algorithms.cuts.volume"]], "cycle_basis() (in module networkx.algorithms.cycles)": [[449, "networkx.algorithms.cycles.cycle_basis"]], "find_cycle() (in module networkx.algorithms.cycles)": [[450, "networkx.algorithms.cycles.find_cycle"]], "minimum_cycle_basis() (in module networkx.algorithms.cycles)": [[451, "networkx.algorithms.cycles.minimum_cycle_basis"]], "recursive_simple_cycles() (in module networkx.algorithms.cycles)": [[452, "networkx.algorithms.cycles.recursive_simple_cycles"]], "simple_cycles() (in module networkx.algorithms.cycles)": [[453, "networkx.algorithms.cycles.simple_cycles"]], "d_separated() (in module networkx.algorithms.d_separation)": [[454, "networkx.algorithms.d_separation.d_separated"]], "all_topological_sorts() (in module networkx.algorithms.dag)": [[455, "networkx.algorithms.dag.all_topological_sorts"]], "ancestors() (in module networkx.algorithms.dag)": [[456, "networkx.algorithms.dag.ancestors"]], "antichains() (in module networkx.algorithms.dag)": [[457, "networkx.algorithms.dag.antichains"]], "dag_longest_path() (in module networkx.algorithms.dag)": [[458, "networkx.algorithms.dag.dag_longest_path"]], "dag_longest_path_length() (in module networkx.algorithms.dag)": [[459, "networkx.algorithms.dag.dag_longest_path_length"]], "dag_to_branching() (in module networkx.algorithms.dag)": [[460, "networkx.algorithms.dag.dag_to_branching"]], "descendants() (in module networkx.algorithms.dag)": [[461, "networkx.algorithms.dag.descendants"]], "is_aperiodic() (in module networkx.algorithms.dag)": [[462, "networkx.algorithms.dag.is_aperiodic"]], "is_directed_acyclic_graph() (in module networkx.algorithms.dag)": [[463, "networkx.algorithms.dag.is_directed_acyclic_graph"]], "lexicographical_topological_sort() (in module networkx.algorithms.dag)": [[464, "networkx.algorithms.dag.lexicographical_topological_sort"]], "topological_generations() (in module networkx.algorithms.dag)": [[465, "networkx.algorithms.dag.topological_generations"]], "topological_sort() (in module networkx.algorithms.dag)": [[466, "networkx.algorithms.dag.topological_sort"]], "transitive_closure() (in module networkx.algorithms.dag)": [[467, "networkx.algorithms.dag.transitive_closure"]], "transitive_closure_dag() (in module networkx.algorithms.dag)": [[468, "networkx.algorithms.dag.transitive_closure_dag"]], "transitive_reduction() (in module networkx.algorithms.dag)": [[469, "networkx.algorithms.dag.transitive_reduction"]], "barycenter() (in module networkx.algorithms.distance_measures)": [[470, "networkx.algorithms.distance_measures.barycenter"]], "center() (in module networkx.algorithms.distance_measures)": [[471, "networkx.algorithms.distance_measures.center"]], "diameter() (in module networkx.algorithms.distance_measures)": [[472, "networkx.algorithms.distance_measures.diameter"]], "eccentricity() (in module networkx.algorithms.distance_measures)": [[473, "networkx.algorithms.distance_measures.eccentricity"]], "periphery() (in module networkx.algorithms.distance_measures)": [[474, "networkx.algorithms.distance_measures.periphery"]], "radius() (in module networkx.algorithms.distance_measures)": [[475, "networkx.algorithms.distance_measures.radius"]], "resistance_distance() (in module networkx.algorithms.distance_measures)": [[476, "networkx.algorithms.distance_measures.resistance_distance"]], "global_parameters() (in module networkx.algorithms.distance_regular)": [[477, "networkx.algorithms.distance_regular.global_parameters"]], "intersection_array() (in module networkx.algorithms.distance_regular)": [[478, "networkx.algorithms.distance_regular.intersection_array"]], "is_distance_regular() (in module networkx.algorithms.distance_regular)": [[479, "networkx.algorithms.distance_regular.is_distance_regular"]], "is_strongly_regular() (in module networkx.algorithms.distance_regular)": [[480, "networkx.algorithms.distance_regular.is_strongly_regular"]], "dominance_frontiers() (in module networkx.algorithms.dominance)": [[481, "networkx.algorithms.dominance.dominance_frontiers"]], "immediate_dominators() (in module networkx.algorithms.dominance)": [[482, "networkx.algorithms.dominance.immediate_dominators"]], "dominating_set() (in module networkx.algorithms.dominating)": [[483, "networkx.algorithms.dominating.dominating_set"]], "is_dominating_set() (in module networkx.algorithms.dominating)": [[484, "networkx.algorithms.dominating.is_dominating_set"]], "efficiency() (in module networkx.algorithms.efficiency_measures)": [[485, "networkx.algorithms.efficiency_measures.efficiency"]], "global_efficiency() (in module networkx.algorithms.efficiency_measures)": [[486, "networkx.algorithms.efficiency_measures.global_efficiency"]], "local_efficiency() (in module networkx.algorithms.efficiency_measures)": [[487, "networkx.algorithms.efficiency_measures.local_efficiency"]], "eulerian_circuit() (in module networkx.algorithms.euler)": [[488, "networkx.algorithms.euler.eulerian_circuit"]], "eulerian_path() (in module networkx.algorithms.euler)": [[489, "networkx.algorithms.euler.eulerian_path"]], "eulerize() (in module networkx.algorithms.euler)": [[490, "networkx.algorithms.euler.eulerize"]], "has_eulerian_path() (in module networkx.algorithms.euler)": [[491, "networkx.algorithms.euler.has_eulerian_path"]], "is_eulerian() (in module networkx.algorithms.euler)": [[492, "networkx.algorithms.euler.is_eulerian"]], "is_semieulerian() (in module networkx.algorithms.euler)": [[493, "networkx.algorithms.euler.is_semieulerian"]], "boykov_kolmogorov() (in module networkx.algorithms.flow)": [[494, "networkx.algorithms.flow.boykov_kolmogorov"]], "build_residual_network() (in module networkx.algorithms.flow)": [[495, "networkx.algorithms.flow.build_residual_network"]], "capacity_scaling() (in module networkx.algorithms.flow)": [[496, "networkx.algorithms.flow.capacity_scaling"]], "cost_of_flow() (in module networkx.algorithms.flow)": [[497, "networkx.algorithms.flow.cost_of_flow"]], "dinitz() (in module networkx.algorithms.flow)": [[498, "networkx.algorithms.flow.dinitz"]], "edmonds_karp() (in module networkx.algorithms.flow)": [[499, "networkx.algorithms.flow.edmonds_karp"]], "gomory_hu_tree() (in module networkx.algorithms.flow)": [[500, "networkx.algorithms.flow.gomory_hu_tree"]], "max_flow_min_cost() (in module networkx.algorithms.flow)": [[501, "networkx.algorithms.flow.max_flow_min_cost"]], "maximum_flow() (in module networkx.algorithms.flow)": [[502, "networkx.algorithms.flow.maximum_flow"]], "maximum_flow_value() (in module networkx.algorithms.flow)": [[503, "networkx.algorithms.flow.maximum_flow_value"]], "min_cost_flow() (in module networkx.algorithms.flow)": [[504, "networkx.algorithms.flow.min_cost_flow"]], "min_cost_flow_cost() (in module networkx.algorithms.flow)": [[505, "networkx.algorithms.flow.min_cost_flow_cost"]], "minimum_cut() (in module networkx.algorithms.flow)": [[506, "networkx.algorithms.flow.minimum_cut"]], "minimum_cut_value() (in module networkx.algorithms.flow)": [[507, "networkx.algorithms.flow.minimum_cut_value"]], "network_simplex() (in module networkx.algorithms.flow)": [[508, "networkx.algorithms.flow.network_simplex"]], "preflow_push() (in module networkx.algorithms.flow)": [[509, "networkx.algorithms.flow.preflow_push"]], "shortest_augmenting_path() (in module networkx.algorithms.flow)": [[510, "networkx.algorithms.flow.shortest_augmenting_path"]], "weisfeiler_lehman_graph_hash() (in module networkx.algorithms.graph_hashing)": [[511, "networkx.algorithms.graph_hashing.weisfeiler_lehman_graph_hash"]], "weisfeiler_lehman_subgraph_hashes() (in module networkx.algorithms.graph_hashing)": [[512, "networkx.algorithms.graph_hashing.weisfeiler_lehman_subgraph_hashes"]], "is_digraphical() (in module networkx.algorithms.graphical)": [[513, "networkx.algorithms.graphical.is_digraphical"]], "is_graphical() (in module networkx.algorithms.graphical)": [[514, "networkx.algorithms.graphical.is_graphical"]], "is_multigraphical() (in module networkx.algorithms.graphical)": [[515, "networkx.algorithms.graphical.is_multigraphical"]], "is_pseudographical() (in module networkx.algorithms.graphical)": [[516, "networkx.algorithms.graphical.is_pseudographical"]], "is_valid_degree_sequence_erdos_gallai() (in module networkx.algorithms.graphical)": [[517, "networkx.algorithms.graphical.is_valid_degree_sequence_erdos_gallai"]], "is_valid_degree_sequence_havel_hakimi() (in module networkx.algorithms.graphical)": [[518, "networkx.algorithms.graphical.is_valid_degree_sequence_havel_hakimi"]], "flow_hierarchy() (in module networkx.algorithms.hierarchy)": [[519, "networkx.algorithms.hierarchy.flow_hierarchy"]], "is_kl_connected() (in module networkx.algorithms.hybrid)": [[520, "networkx.algorithms.hybrid.is_kl_connected"]], "kl_connected_subgraph() (in module networkx.algorithms.hybrid)": [[521, "networkx.algorithms.hybrid.kl_connected_subgraph"]], "is_isolate() (in module networkx.algorithms.isolate)": [[522, "networkx.algorithms.isolate.is_isolate"]], "isolates() (in module networkx.algorithms.isolate)": [[523, "networkx.algorithms.isolate.isolates"]], "number_of_isolates() (in module networkx.algorithms.isolate)": [[524, "networkx.algorithms.isolate.number_of_isolates"]], "__init__() (digraphmatcher method)": [[525, "networkx.algorithms.isomorphism.DiGraphMatcher.__init__"]], "candidate_pairs_iter() (digraphmatcher method)": [[526, "networkx.algorithms.isomorphism.DiGraphMatcher.candidate_pairs_iter"]], "initialize() (digraphmatcher method)": [[527, "networkx.algorithms.isomorphism.DiGraphMatcher.initialize"]], "is_isomorphic() (digraphmatcher method)": [[528, "networkx.algorithms.isomorphism.DiGraphMatcher.is_isomorphic"]], "isomorphisms_iter() (digraphmatcher method)": [[529, "networkx.algorithms.isomorphism.DiGraphMatcher.isomorphisms_iter"]], "match() (digraphmatcher method)": [[530, "networkx.algorithms.isomorphism.DiGraphMatcher.match"]], "semantic_feasibility() (digraphmatcher method)": [[531, "networkx.algorithms.isomorphism.DiGraphMatcher.semantic_feasibility"]], "subgraph_is_isomorphic() (digraphmatcher method)": [[532, "networkx.algorithms.isomorphism.DiGraphMatcher.subgraph_is_isomorphic"]], "subgraph_isomorphisms_iter() (digraphmatcher method)": [[533, "networkx.algorithms.isomorphism.DiGraphMatcher.subgraph_isomorphisms_iter"]], "syntactic_feasibility() (digraphmatcher method)": [[534, "networkx.algorithms.isomorphism.DiGraphMatcher.syntactic_feasibility"]], "__init__() (graphmatcher method)": [[535, "networkx.algorithms.isomorphism.GraphMatcher.__init__"]], "candidate_pairs_iter() (graphmatcher method)": [[536, "networkx.algorithms.isomorphism.GraphMatcher.candidate_pairs_iter"]], "initialize() (graphmatcher method)": [[537, "networkx.algorithms.isomorphism.GraphMatcher.initialize"]], "is_isomorphic() (graphmatcher method)": [[538, "networkx.algorithms.isomorphism.GraphMatcher.is_isomorphic"]], "isomorphisms_iter() (graphmatcher method)": [[539, "networkx.algorithms.isomorphism.GraphMatcher.isomorphisms_iter"]], "match() (graphmatcher method)": [[540, "networkx.algorithms.isomorphism.GraphMatcher.match"]], "semantic_feasibility() (graphmatcher method)": [[541, "networkx.algorithms.isomorphism.GraphMatcher.semantic_feasibility"]], "subgraph_is_isomorphic() (graphmatcher method)": [[542, "networkx.algorithms.isomorphism.GraphMatcher.subgraph_is_isomorphic"]], "subgraph_isomorphisms_iter() (graphmatcher method)": [[543, "networkx.algorithms.isomorphism.GraphMatcher.subgraph_isomorphisms_iter"]], "syntactic_feasibility() (graphmatcher method)": [[544, "networkx.algorithms.isomorphism.GraphMatcher.syntactic_feasibility"]], "ismags (class in networkx.algorithms.isomorphism)": [[545, "networkx.algorithms.isomorphism.ISMAGS"]], "__init__() (ismags method)": [[545, "networkx.algorithms.isomorphism.ISMAGS.__init__"]], "categorical_edge_match() (in module networkx.algorithms.isomorphism)": [[546, "networkx.algorithms.isomorphism.categorical_edge_match"]], "categorical_multiedge_match() (in module networkx.algorithms.isomorphism)": [[547, "networkx.algorithms.isomorphism.categorical_multiedge_match"]], "categorical_node_match() (in module networkx.algorithms.isomorphism)": [[548, "networkx.algorithms.isomorphism.categorical_node_match"]], "could_be_isomorphic() (in module networkx.algorithms.isomorphism)": [[549, "networkx.algorithms.isomorphism.could_be_isomorphic"]], "fast_could_be_isomorphic() (in module networkx.algorithms.isomorphism)": [[550, "networkx.algorithms.isomorphism.fast_could_be_isomorphic"]], "faster_could_be_isomorphic() (in module networkx.algorithms.isomorphism)": [[551, "networkx.algorithms.isomorphism.faster_could_be_isomorphic"]], "generic_edge_match() (in module networkx.algorithms.isomorphism)": [[552, "networkx.algorithms.isomorphism.generic_edge_match"]], "generic_multiedge_match() (in module networkx.algorithms.isomorphism)": [[553, "networkx.algorithms.isomorphism.generic_multiedge_match"]], "generic_node_match() (in module networkx.algorithms.isomorphism)": [[554, "networkx.algorithms.isomorphism.generic_node_match"]], "is_isomorphic() (in module networkx.algorithms.isomorphism)": [[555, "networkx.algorithms.isomorphism.is_isomorphic"]], "numerical_edge_match() (in module networkx.algorithms.isomorphism)": [[556, "networkx.algorithms.isomorphism.numerical_edge_match"]], "numerical_multiedge_match() (in module networkx.algorithms.isomorphism)": [[557, "networkx.algorithms.isomorphism.numerical_multiedge_match"]], "numerical_node_match() (in module networkx.algorithms.isomorphism)": [[558, "networkx.algorithms.isomorphism.numerical_node_match"]], "rooted_tree_isomorphism() (in module networkx.algorithms.isomorphism.tree_isomorphism)": [[559, "networkx.algorithms.isomorphism.tree_isomorphism.rooted_tree_isomorphism"]], "tree_isomorphism() (in module networkx.algorithms.isomorphism.tree_isomorphism)": [[560, "networkx.algorithms.isomorphism.tree_isomorphism.tree_isomorphism"]], "vf2pp_all_isomorphisms() (in module networkx.algorithms.isomorphism.vf2pp)": [[561, "networkx.algorithms.isomorphism.vf2pp.vf2pp_all_isomorphisms"]], "vf2pp_is_isomorphic() (in module networkx.algorithms.isomorphism.vf2pp)": [[562, "networkx.algorithms.isomorphism.vf2pp.vf2pp_is_isomorphic"]], "vf2pp_isomorphism() (in module networkx.algorithms.isomorphism.vf2pp)": [[563, "networkx.algorithms.isomorphism.vf2pp.vf2pp_isomorphism"]], "hits() (in module networkx.algorithms.link_analysis.hits_alg)": [[564, "networkx.algorithms.link_analysis.hits_alg.hits"]], "google_matrix() (in module networkx.algorithms.link_analysis.pagerank_alg)": [[565, "networkx.algorithms.link_analysis.pagerank_alg.google_matrix"]], "pagerank() (in module networkx.algorithms.link_analysis.pagerank_alg)": [[566, "networkx.algorithms.link_analysis.pagerank_alg.pagerank"]], "adamic_adar_index() (in module networkx.algorithms.link_prediction)": [[567, "networkx.algorithms.link_prediction.adamic_adar_index"]], "cn_soundarajan_hopcroft() (in module networkx.algorithms.link_prediction)": [[568, "networkx.algorithms.link_prediction.cn_soundarajan_hopcroft"]], "common_neighbor_centrality() (in module networkx.algorithms.link_prediction)": [[569, "networkx.algorithms.link_prediction.common_neighbor_centrality"]], "jaccard_coefficient() (in module networkx.algorithms.link_prediction)": [[570, "networkx.algorithms.link_prediction.jaccard_coefficient"]], "preferential_attachment() (in module networkx.algorithms.link_prediction)": [[571, "networkx.algorithms.link_prediction.preferential_attachment"]], "ra_index_soundarajan_hopcroft() (in module networkx.algorithms.link_prediction)": [[572, "networkx.algorithms.link_prediction.ra_index_soundarajan_hopcroft"]], "resource_allocation_index() (in module networkx.algorithms.link_prediction)": [[573, "networkx.algorithms.link_prediction.resource_allocation_index"]], "within_inter_cluster() (in module networkx.algorithms.link_prediction)": [[574, "networkx.algorithms.link_prediction.within_inter_cluster"]], "all_pairs_lowest_common_ancestor() (in module networkx.algorithms.lowest_common_ancestors)": [[575, "networkx.algorithms.lowest_common_ancestors.all_pairs_lowest_common_ancestor"]], "lowest_common_ancestor() (in module networkx.algorithms.lowest_common_ancestors)": [[576, "networkx.algorithms.lowest_common_ancestors.lowest_common_ancestor"]], "tree_all_pairs_lowest_common_ancestor() (in module networkx.algorithms.lowest_common_ancestors)": [[577, "networkx.algorithms.lowest_common_ancestors.tree_all_pairs_lowest_common_ancestor"]], "is_matching() (in module networkx.algorithms.matching)": [[578, "networkx.algorithms.matching.is_matching"]], "is_maximal_matching() (in module networkx.algorithms.matching)": [[579, "networkx.algorithms.matching.is_maximal_matching"]], "is_perfect_matching() (in module networkx.algorithms.matching)": [[580, "networkx.algorithms.matching.is_perfect_matching"]], "max_weight_matching() (in module networkx.algorithms.matching)": [[581, "networkx.algorithms.matching.max_weight_matching"]], "maximal_matching() (in module networkx.algorithms.matching)": [[582, "networkx.algorithms.matching.maximal_matching"]], "min_weight_matching() (in module networkx.algorithms.matching)": [[583, "networkx.algorithms.matching.min_weight_matching"]], "contracted_edge() (in module networkx.algorithms.minors)": [[584, "networkx.algorithms.minors.contracted_edge"]], "contracted_nodes() (in module networkx.algorithms.minors)": [[585, "networkx.algorithms.minors.contracted_nodes"]], "equivalence_classes() (in module networkx.algorithms.minors)": [[586, "networkx.algorithms.minors.equivalence_classes"]], "identified_nodes() (in module networkx.algorithms.minors)": [[587, "networkx.algorithms.minors.identified_nodes"]], "quotient_graph() (in module networkx.algorithms.minors)": [[588, "networkx.algorithms.minors.quotient_graph"]], "maximal_independent_set() (in module networkx.algorithms.mis)": [[589, "networkx.algorithms.mis.maximal_independent_set"]], "moral_graph() (in module networkx.algorithms.moral)": [[590, "networkx.algorithms.moral.moral_graph"]], "harmonic_function() (in module networkx.algorithms.node_classification)": [[591, "networkx.algorithms.node_classification.harmonic_function"]], "local_and_global_consistency() (in module networkx.algorithms.node_classification)": [[592, "networkx.algorithms.node_classification.local_and_global_consistency"]], "non_randomness() (in module networkx.algorithms.non_randomness)": [[593, "networkx.algorithms.non_randomness.non_randomness"]], "compose_all() (in module networkx.algorithms.operators.all)": [[594, "networkx.algorithms.operators.all.compose_all"]], "disjoint_union_all() (in module networkx.algorithms.operators.all)": [[595, "networkx.algorithms.operators.all.disjoint_union_all"]], "intersection_all() (in module networkx.algorithms.operators.all)": [[596, "networkx.algorithms.operators.all.intersection_all"]], "union_all() (in module networkx.algorithms.operators.all)": [[597, "networkx.algorithms.operators.all.union_all"]], "compose() (in module networkx.algorithms.operators.binary)": [[598, "networkx.algorithms.operators.binary.compose"]], "difference() (in module networkx.algorithms.operators.binary)": [[599, "networkx.algorithms.operators.binary.difference"]], "disjoint_union() (in module networkx.algorithms.operators.binary)": [[600, "networkx.algorithms.operators.binary.disjoint_union"]], "full_join() (in module networkx.algorithms.operators.binary)": [[601, "networkx.algorithms.operators.binary.full_join"]], "intersection() (in module networkx.algorithms.operators.binary)": [[602, "networkx.algorithms.operators.binary.intersection"]], "symmetric_difference() (in module networkx.algorithms.operators.binary)": [[603, "networkx.algorithms.operators.binary.symmetric_difference"]], "union() (in module networkx.algorithms.operators.binary)": [[604, "networkx.algorithms.operators.binary.union"]], "cartesian_product() (in module networkx.algorithms.operators.product)": [[605, "networkx.algorithms.operators.product.cartesian_product"]], "corona_product() (in module networkx.algorithms.operators.product)": [[606, "networkx.algorithms.operators.product.corona_product"]], "lexicographic_product() (in module networkx.algorithms.operators.product)": [[607, "networkx.algorithms.operators.product.lexicographic_product"]], "power() (in module networkx.algorithms.operators.product)": [[608, "networkx.algorithms.operators.product.power"]], "rooted_product() (in module networkx.algorithms.operators.product)": [[609, "networkx.algorithms.operators.product.rooted_product"]], "strong_product() (in module networkx.algorithms.operators.product)": [[610, "networkx.algorithms.operators.product.strong_product"]], "tensor_product() (in module networkx.algorithms.operators.product)": [[611, "networkx.algorithms.operators.product.tensor_product"]], "complement() (in module networkx.algorithms.operators.unary)": [[612, "networkx.algorithms.operators.unary.complement"]], "reverse() (in module networkx.algorithms.operators.unary)": [[613, "networkx.algorithms.operators.unary.reverse"]], "combinatorial_embedding_to_pos() (in module networkx.algorithms.planar_drawing)": [[614, "networkx.algorithms.planar_drawing.combinatorial_embedding_to_pos"]], "planarembedding (class in networkx.algorithms.planarity)": [[615, "networkx.algorithms.planarity.PlanarEmbedding"]], "__init__() (planarembedding method)": [[615, "networkx.algorithms.planarity.PlanarEmbedding.__init__"]], "check_planarity() (in module networkx.algorithms.planarity)": [[616, "networkx.algorithms.planarity.check_planarity"]], "is_planar() (in module networkx.algorithms.planarity)": [[617, "networkx.algorithms.planarity.is_planar"]], "chromatic_polynomial() (in module networkx.algorithms.polynomials)": [[618, "networkx.algorithms.polynomials.chromatic_polynomial"]], "tutte_polynomial() (in module networkx.algorithms.polynomials)": [[619, "networkx.algorithms.polynomials.tutte_polynomial"]], "overall_reciprocity() (in module networkx.algorithms.reciprocity)": [[620, "networkx.algorithms.reciprocity.overall_reciprocity"]], "reciprocity() (in module networkx.algorithms.reciprocity)": [[621, "networkx.algorithms.reciprocity.reciprocity"]], "is_k_regular() (in module networkx.algorithms.regular)": [[622, "networkx.algorithms.regular.is_k_regular"]], "is_regular() (in module networkx.algorithms.regular)": [[623, "networkx.algorithms.regular.is_regular"]], "k_factor() (in module networkx.algorithms.regular)": [[624, "networkx.algorithms.regular.k_factor"]], "rich_club_coefficient() (in module networkx.algorithms.richclub)": [[625, "networkx.algorithms.richclub.rich_club_coefficient"]], "astar_path() (in module networkx.algorithms.shortest_paths.astar)": [[626, "networkx.algorithms.shortest_paths.astar.astar_path"]], "astar_path_length() (in module networkx.algorithms.shortest_paths.astar)": [[627, "networkx.algorithms.shortest_paths.astar.astar_path_length"]], "floyd_warshall() (in module networkx.algorithms.shortest_paths.dense)": [[628, "networkx.algorithms.shortest_paths.dense.floyd_warshall"]], "floyd_warshall_numpy() (in module networkx.algorithms.shortest_paths.dense)": [[629, "networkx.algorithms.shortest_paths.dense.floyd_warshall_numpy"]], "floyd_warshall_predecessor_and_distance() (in module networkx.algorithms.shortest_paths.dense)": [[630, "networkx.algorithms.shortest_paths.dense.floyd_warshall_predecessor_and_distance"]], "reconstruct_path() (in module networkx.algorithms.shortest_paths.dense)": [[631, "networkx.algorithms.shortest_paths.dense.reconstruct_path"]], "all_shortest_paths() (in module networkx.algorithms.shortest_paths.generic)": [[632, "networkx.algorithms.shortest_paths.generic.all_shortest_paths"]], "average_shortest_path_length() (in module networkx.algorithms.shortest_paths.generic)": [[633, "networkx.algorithms.shortest_paths.generic.average_shortest_path_length"]], "has_path() (in module networkx.algorithms.shortest_paths.generic)": [[634, "networkx.algorithms.shortest_paths.generic.has_path"]], "shortest_path() (in module networkx.algorithms.shortest_paths.generic)": [[635, "networkx.algorithms.shortest_paths.generic.shortest_path"]], "shortest_path_length() (in module networkx.algorithms.shortest_paths.generic)": [[636, "networkx.algorithms.shortest_paths.generic.shortest_path_length"]], "all_pairs_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[637, "networkx.algorithms.shortest_paths.unweighted.all_pairs_shortest_path"]], "all_pairs_shortest_path_length() (in module networkx.algorithms.shortest_paths.unweighted)": [[638, "networkx.algorithms.shortest_paths.unweighted.all_pairs_shortest_path_length"]], "bidirectional_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[639, "networkx.algorithms.shortest_paths.unweighted.bidirectional_shortest_path"]], "predecessor() (in module networkx.algorithms.shortest_paths.unweighted)": [[640, "networkx.algorithms.shortest_paths.unweighted.predecessor"]], "single_source_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[641, "networkx.algorithms.shortest_paths.unweighted.single_source_shortest_path"]], "single_source_shortest_path_length() (in module networkx.algorithms.shortest_paths.unweighted)": [[642, "networkx.algorithms.shortest_paths.unweighted.single_source_shortest_path_length"]], "single_target_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[643, "networkx.algorithms.shortest_paths.unweighted.single_target_shortest_path"]], "single_target_shortest_path_length() (in module networkx.algorithms.shortest_paths.unweighted)": [[644, "networkx.algorithms.shortest_paths.unweighted.single_target_shortest_path_length"]], "all_pairs_bellman_ford_path() (in module networkx.algorithms.shortest_paths.weighted)": [[645, "networkx.algorithms.shortest_paths.weighted.all_pairs_bellman_ford_path"]], "all_pairs_bellman_ford_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[646, "networkx.algorithms.shortest_paths.weighted.all_pairs_bellman_ford_path_length"]], "all_pairs_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[647, "networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra"]], "all_pairs_dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[648, "networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra_path"]], "all_pairs_dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[649, "networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra_path_length"]], "bellman_ford_path() (in module networkx.algorithms.shortest_paths.weighted)": [[650, "networkx.algorithms.shortest_paths.weighted.bellman_ford_path"]], "bellman_ford_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[651, "networkx.algorithms.shortest_paths.weighted.bellman_ford_path_length"]], "bellman_ford_predecessor_and_distance() (in module networkx.algorithms.shortest_paths.weighted)": [[652, "networkx.algorithms.shortest_paths.weighted.bellman_ford_predecessor_and_distance"]], "bidirectional_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[653, "networkx.algorithms.shortest_paths.weighted.bidirectional_dijkstra"]], "dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[654, "networkx.algorithms.shortest_paths.weighted.dijkstra_path"]], "dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[655, "networkx.algorithms.shortest_paths.weighted.dijkstra_path_length"]], "dijkstra_predecessor_and_distance() (in module networkx.algorithms.shortest_paths.weighted)": [[656, "networkx.algorithms.shortest_paths.weighted.dijkstra_predecessor_and_distance"]], "find_negative_cycle() (in module networkx.algorithms.shortest_paths.weighted)": [[657, "networkx.algorithms.shortest_paths.weighted.find_negative_cycle"]], "goldberg_radzik() (in module networkx.algorithms.shortest_paths.weighted)": [[658, "networkx.algorithms.shortest_paths.weighted.goldberg_radzik"]], "johnson() (in module networkx.algorithms.shortest_paths.weighted)": [[659, "networkx.algorithms.shortest_paths.weighted.johnson"]], "multi_source_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[660, "networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra"]], "multi_source_dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[661, "networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra_path"]], "multi_source_dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[662, "networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra_path_length"]], "negative_edge_cycle() (in module networkx.algorithms.shortest_paths.weighted)": [[663, "networkx.algorithms.shortest_paths.weighted.negative_edge_cycle"]], "single_source_bellman_ford() (in module networkx.algorithms.shortest_paths.weighted)": [[664, "networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford"]], "single_source_bellman_ford_path() (in module networkx.algorithms.shortest_paths.weighted)": [[665, "networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford_path"]], "single_source_bellman_ford_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[666, "networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford_path_length"]], "single_source_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[667, "networkx.algorithms.shortest_paths.weighted.single_source_dijkstra"]], "single_source_dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[668, "networkx.algorithms.shortest_paths.weighted.single_source_dijkstra_path"]], "single_source_dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[669, "networkx.algorithms.shortest_paths.weighted.single_source_dijkstra_path_length"]], "generate_random_paths() (in module networkx.algorithms.similarity)": [[670, "networkx.algorithms.similarity.generate_random_paths"]], "graph_edit_distance() (in module networkx.algorithms.similarity)": [[671, "networkx.algorithms.similarity.graph_edit_distance"]], "optimal_edit_paths() (in module networkx.algorithms.similarity)": [[672, "networkx.algorithms.similarity.optimal_edit_paths"]], "optimize_edit_paths() (in module networkx.algorithms.similarity)": [[673, "networkx.algorithms.similarity.optimize_edit_paths"]], "optimize_graph_edit_distance() (in module networkx.algorithms.similarity)": [[674, "networkx.algorithms.similarity.optimize_graph_edit_distance"]], "panther_similarity() (in module networkx.algorithms.similarity)": [[675, "networkx.algorithms.similarity.panther_similarity"]], "simrank_similarity() (in module networkx.algorithms.similarity)": [[676, "networkx.algorithms.similarity.simrank_similarity"]], "all_simple_edge_paths() (in module networkx.algorithms.simple_paths)": [[677, "networkx.algorithms.simple_paths.all_simple_edge_paths"]], "all_simple_paths() (in module networkx.algorithms.simple_paths)": [[678, "networkx.algorithms.simple_paths.all_simple_paths"]], "is_simple_path() (in module networkx.algorithms.simple_paths)": [[679, "networkx.algorithms.simple_paths.is_simple_path"]], "shortest_simple_paths() (in module networkx.algorithms.simple_paths)": [[680, "networkx.algorithms.simple_paths.shortest_simple_paths"]], "lattice_reference() (in module networkx.algorithms.smallworld)": [[681, "networkx.algorithms.smallworld.lattice_reference"]], "omega() (in module networkx.algorithms.smallworld)": [[682, "networkx.algorithms.smallworld.omega"]], "random_reference() (in module networkx.algorithms.smallworld)": [[683, "networkx.algorithms.smallworld.random_reference"]], "sigma() (in module networkx.algorithms.smallworld)": [[684, "networkx.algorithms.smallworld.sigma"]], "s_metric() (in module networkx.algorithms.smetric)": [[685, "networkx.algorithms.smetric.s_metric"]], "spanner() (in module networkx.algorithms.sparsifiers)": [[686, "networkx.algorithms.sparsifiers.spanner"]], "constraint() (in module networkx.algorithms.structuralholes)": [[687, "networkx.algorithms.structuralholes.constraint"]], "effective_size() (in module networkx.algorithms.structuralholes)": [[688, "networkx.algorithms.structuralholes.effective_size"]], "local_constraint() (in module networkx.algorithms.structuralholes)": [[689, "networkx.algorithms.structuralholes.local_constraint"]], "dedensify() (in module networkx.algorithms.summarization)": [[690, "networkx.algorithms.summarization.dedensify"]], "snap_aggregation() (in module networkx.algorithms.summarization)": [[691, "networkx.algorithms.summarization.snap_aggregation"]], "connected_double_edge_swap() (in module networkx.algorithms.swap)": [[692, "networkx.algorithms.swap.connected_double_edge_swap"]], "directed_edge_swap() (in module networkx.algorithms.swap)": [[693, "networkx.algorithms.swap.directed_edge_swap"]], "double_edge_swap() (in module networkx.algorithms.swap)": [[694, "networkx.algorithms.swap.double_edge_swap"]], "find_threshold_graph() (in module networkx.algorithms.threshold)": [[695, "networkx.algorithms.threshold.find_threshold_graph"]], "is_threshold_graph() (in module networkx.algorithms.threshold)": [[696, "networkx.algorithms.threshold.is_threshold_graph"]], "hamiltonian_path() (in module networkx.algorithms.tournament)": [[697, "networkx.algorithms.tournament.hamiltonian_path"]], "is_reachable() (in module networkx.algorithms.tournament)": [[698, "networkx.algorithms.tournament.is_reachable"]], "is_strongly_connected() (in module networkx.algorithms.tournament)": [[699, "networkx.algorithms.tournament.is_strongly_connected"]], "is_tournament() (in module networkx.algorithms.tournament)": [[700, "networkx.algorithms.tournament.is_tournament"]], "random_tournament() (in module networkx.algorithms.tournament)": [[701, "networkx.algorithms.tournament.random_tournament"]], "score_sequence() (in module networkx.algorithms.tournament)": [[702, "networkx.algorithms.tournament.score_sequence"]], "bfs_beam_edges() (in module networkx.algorithms.traversal.beamsearch)": [[703, "networkx.algorithms.traversal.beamsearch.bfs_beam_edges"]], "bfs_edges() (in module networkx.algorithms.traversal.breadth_first_search)": [[704, "networkx.algorithms.traversal.breadth_first_search.bfs_edges"]], "bfs_layers() (in module networkx.algorithms.traversal.breadth_first_search)": [[705, "networkx.algorithms.traversal.breadth_first_search.bfs_layers"]], "bfs_predecessors() (in module networkx.algorithms.traversal.breadth_first_search)": [[706, "networkx.algorithms.traversal.breadth_first_search.bfs_predecessors"]], "bfs_successors() (in module networkx.algorithms.traversal.breadth_first_search)": [[707, "networkx.algorithms.traversal.breadth_first_search.bfs_successors"]], "bfs_tree() (in module networkx.algorithms.traversal.breadth_first_search)": [[708, "networkx.algorithms.traversal.breadth_first_search.bfs_tree"]], "descendants_at_distance() (in module networkx.algorithms.traversal.breadth_first_search)": [[709, "networkx.algorithms.traversal.breadth_first_search.descendants_at_distance"]], "dfs_edges() (in module networkx.algorithms.traversal.depth_first_search)": [[710, "networkx.algorithms.traversal.depth_first_search.dfs_edges"]], "dfs_labeled_edges() (in module networkx.algorithms.traversal.depth_first_search)": [[711, "networkx.algorithms.traversal.depth_first_search.dfs_labeled_edges"]], "dfs_postorder_nodes() (in module networkx.algorithms.traversal.depth_first_search)": [[712, "networkx.algorithms.traversal.depth_first_search.dfs_postorder_nodes"]], "dfs_predecessors() (in module networkx.algorithms.traversal.depth_first_search)": [[713, "networkx.algorithms.traversal.depth_first_search.dfs_predecessors"]], "dfs_preorder_nodes() (in module networkx.algorithms.traversal.depth_first_search)": [[714, "networkx.algorithms.traversal.depth_first_search.dfs_preorder_nodes"]], "dfs_successors() (in module networkx.algorithms.traversal.depth_first_search)": [[715, "networkx.algorithms.traversal.depth_first_search.dfs_successors"]], "dfs_tree() (in module networkx.algorithms.traversal.depth_first_search)": [[716, "networkx.algorithms.traversal.depth_first_search.dfs_tree"]], "edge_bfs() (in module networkx.algorithms.traversal.edgebfs)": [[717, "networkx.algorithms.traversal.edgebfs.edge_bfs"]], "edge_dfs() (in module networkx.algorithms.traversal.edgedfs)": [[718, "networkx.algorithms.traversal.edgedfs.edge_dfs"]], "arborescenceiterator (class in networkx.algorithms.tree.branchings)": [[719, "networkx.algorithms.tree.branchings.ArborescenceIterator"]], "__init__() (arborescenceiterator method)": [[719, "networkx.algorithms.tree.branchings.ArborescenceIterator.__init__"]], "edmonds (class in networkx.algorithms.tree.branchings)": [[720, "networkx.algorithms.tree.branchings.Edmonds"]], "__init__() (edmonds method)": [[720, "networkx.algorithms.tree.branchings.Edmonds.__init__"]], "branching_weight() (in module networkx.algorithms.tree.branchings)": [[721, "networkx.algorithms.tree.branchings.branching_weight"]], "greedy_branching() (in module networkx.algorithms.tree.branchings)": [[722, "networkx.algorithms.tree.branchings.greedy_branching"]], "maximum_branching() (in module networkx.algorithms.tree.branchings)": [[723, "networkx.algorithms.tree.branchings.maximum_branching"]], "maximum_spanning_arborescence() (in module networkx.algorithms.tree.branchings)": [[724, "networkx.algorithms.tree.branchings.maximum_spanning_arborescence"]], "minimum_branching() (in module networkx.algorithms.tree.branchings)": [[725, "networkx.algorithms.tree.branchings.minimum_branching"]], "minimum_spanning_arborescence() (in module networkx.algorithms.tree.branchings)": [[726, "networkx.algorithms.tree.branchings.minimum_spanning_arborescence"]], "notatree": [[727, "networkx.algorithms.tree.coding.NotATree"]], "from_nested_tuple() (in module networkx.algorithms.tree.coding)": [[728, "networkx.algorithms.tree.coding.from_nested_tuple"]], "from_prufer_sequence() (in module networkx.algorithms.tree.coding)": [[729, "networkx.algorithms.tree.coding.from_prufer_sequence"]], "to_nested_tuple() (in module networkx.algorithms.tree.coding)": [[730, "networkx.algorithms.tree.coding.to_nested_tuple"]], "to_prufer_sequence() (in module networkx.algorithms.tree.coding)": [[731, "networkx.algorithms.tree.coding.to_prufer_sequence"]], "junction_tree() (in module networkx.algorithms.tree.decomposition)": [[732, "networkx.algorithms.tree.decomposition.junction_tree"]], "spanningtreeiterator (class in networkx.algorithms.tree.mst)": [[733, "networkx.algorithms.tree.mst.SpanningTreeIterator"]], "__init__() (spanningtreeiterator method)": [[733, "networkx.algorithms.tree.mst.SpanningTreeIterator.__init__"]], "maximum_spanning_edges() (in module networkx.algorithms.tree.mst)": [[734, "networkx.algorithms.tree.mst.maximum_spanning_edges"]], "maximum_spanning_tree() (in module networkx.algorithms.tree.mst)": [[735, "networkx.algorithms.tree.mst.maximum_spanning_tree"]], "minimum_spanning_edges() (in module networkx.algorithms.tree.mst)": [[736, "networkx.algorithms.tree.mst.minimum_spanning_edges"]], "minimum_spanning_tree() (in module networkx.algorithms.tree.mst)": [[737, "networkx.algorithms.tree.mst.minimum_spanning_tree"]], "random_spanning_tree() (in module networkx.algorithms.tree.mst)": [[738, "networkx.algorithms.tree.mst.random_spanning_tree"]], "join() (in module networkx.algorithms.tree.operations)": [[739, "networkx.algorithms.tree.operations.join"]], "is_arborescence() (in module networkx.algorithms.tree.recognition)": [[740, "networkx.algorithms.tree.recognition.is_arborescence"]], "is_branching() (in module networkx.algorithms.tree.recognition)": [[741, "networkx.algorithms.tree.recognition.is_branching"]], "is_forest() (in module networkx.algorithms.tree.recognition)": [[742, "networkx.algorithms.tree.recognition.is_forest"]], "is_tree() (in module networkx.algorithms.tree.recognition)": [[743, "networkx.algorithms.tree.recognition.is_tree"]], "all_triads() (in module networkx.algorithms.triads)": [[744, "networkx.algorithms.triads.all_triads"]], "all_triplets() (in module networkx.algorithms.triads)": [[745, "networkx.algorithms.triads.all_triplets"]], "is_triad() (in module networkx.algorithms.triads)": [[746, "networkx.algorithms.triads.is_triad"]], "random_triad() (in module networkx.algorithms.triads)": [[747, "networkx.algorithms.triads.random_triad"]], "triad_type() (in module networkx.algorithms.triads)": [[748, "networkx.algorithms.triads.triad_type"]], "triadic_census() (in module networkx.algorithms.triads)": [[749, "networkx.algorithms.triads.triadic_census"]], "triads_by_type() (in module networkx.algorithms.triads)": [[750, "networkx.algorithms.triads.triads_by_type"]], "closeness_vitality() (in module networkx.algorithms.vitality)": [[751, "networkx.algorithms.vitality.closeness_vitality"]], "voronoi_cells() (in module networkx.algorithms.voronoi)": [[752, "networkx.algorithms.voronoi.voronoi_cells"]], "wiener_index() (in module networkx.algorithms.wiener)": [[753, "networkx.algorithms.wiener.wiener_index"]], "networkx.algorithms.graph_hashing": [[754, "module-networkx.algorithms.graph_hashing"]], "networkx.algorithms.graphical": [[755, "module-networkx.algorithms.graphical"]], "networkx.algorithms.hierarchy": [[756, "module-networkx.algorithms.hierarchy"]], "networkx.algorithms.hybrid": [[757, "module-networkx.algorithms.hybrid"]], "networkx.algorithms.isolate": [[759, "module-networkx.algorithms.isolate"]], "networkx.algorithms.isomorphism": [[760, "module-networkx.algorithms.isomorphism"]], "networkx.algorithms.isomorphism.tree_isomorphism": [[760, "module-networkx.algorithms.isomorphism.tree_isomorphism"]], "networkx.algorithms.isomorphism.vf2pp": [[760, "module-networkx.algorithms.isomorphism.vf2pp"]], "networkx.algorithms.isomorphism.ismags": [[761, "module-networkx.algorithms.isomorphism.ismags"]], "networkx.algorithms.isomorphism.isomorphvf2": [[762, "module-networkx.algorithms.isomorphism.isomorphvf2"]], "networkx.algorithms.link_analysis.hits_alg": [[763, "module-networkx.algorithms.link_analysis.hits_alg"]], "networkx.algorithms.link_analysis.pagerank_alg": [[763, "module-networkx.algorithms.link_analysis.pagerank_alg"]], "networkx.algorithms.link_prediction": [[764, "module-networkx.algorithms.link_prediction"]], "networkx.algorithms.lowest_common_ancestors": [[765, "module-networkx.algorithms.lowest_common_ancestors"]], "networkx.algorithms.matching": [[766, "module-networkx.algorithms.matching"]], "networkx.algorithms.minors": [[767, "module-networkx.algorithms.minors"]], "networkx.algorithms.mis": [[768, "module-networkx.algorithms.mis"]], "networkx.algorithms.moral": [[769, "module-networkx.algorithms.moral"]], "networkx.algorithms.node_classification": [[770, "module-networkx.algorithms.node_classification"]], "networkx.algorithms.non_randomness": [[771, "module-networkx.algorithms.non_randomness"]], "networkx.algorithms.operators.all": [[772, "module-networkx.algorithms.operators.all"]], "networkx.algorithms.operators.binary": [[772, "module-networkx.algorithms.operators.binary"]], "networkx.algorithms.operators.product": [[772, "module-networkx.algorithms.operators.product"]], "networkx.algorithms.operators.unary": [[772, 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"networkx.algorithms.shortest_paths.weighted": [[779, "module-networkx.algorithms.shortest_paths.weighted"]], "networkx.algorithms.similarity": [[780, "module-networkx.algorithms.similarity"]], "networkx.algorithms.simple_paths": [[781, "module-networkx.algorithms.simple_paths"]], "networkx.algorithms.smallworld": [[782, "module-networkx.algorithms.smallworld"]], "networkx.algorithms.smetric": [[783, "module-networkx.algorithms.smetric"]], "networkx.algorithms.sparsifiers": [[784, "module-networkx.algorithms.sparsifiers"]], "networkx.algorithms.structuralholes": [[785, "module-networkx.algorithms.structuralholes"]], "networkx.algorithms.summarization": [[786, "module-networkx.algorithms.summarization"]], "networkx.algorithms.swap": [[787, "module-networkx.algorithms.swap"]], "networkx.algorithms.threshold": [[788, "module-networkx.algorithms.threshold"]], "networkx.algorithms.tournament": [[789, "module-networkx.algorithms.tournament"]], "networkx.algorithms.traversal.beamsearch": 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"networkx.Graph.to_undirected"]], "update() (graph method)": [[928, "networkx.Graph.update"]], "__contains__() (multidigraph method)": [[929, "networkx.MultiDiGraph.__contains__"]], "__getitem__() (multidigraph method)": [[930, "networkx.MultiDiGraph.__getitem__"]], "__init__() (multidigraph method)": [[931, "networkx.MultiDiGraph.__init__"]], "__iter__() (multidigraph method)": [[932, "networkx.MultiDiGraph.__iter__"]], "__len__() (multidigraph method)": [[933, "networkx.MultiDiGraph.__len__"]], "add_edge() (multidigraph method)": [[934, "networkx.MultiDiGraph.add_edge"]], "add_edges_from() (multidigraph method)": [[935, "networkx.MultiDiGraph.add_edges_from"]], "add_node() (multidigraph method)": [[936, "networkx.MultiDiGraph.add_node"]], "add_nodes_from() (multidigraph method)": [[937, "networkx.MultiDiGraph.add_nodes_from"]], "add_weighted_edges_from() (multidigraph method)": [[938, "networkx.MultiDiGraph.add_weighted_edges_from"]], "adj (multidigraph property)": [[939, 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"networkx.MultiDiGraph.remove_edges_from"]], "remove_node() (multidigraph method)": [[964, "networkx.MultiDiGraph.remove_node"]], "remove_nodes_from() (multidigraph method)": [[965, "networkx.MultiDiGraph.remove_nodes_from"]], "reverse() (multidigraph method)": [[966, "networkx.MultiDiGraph.reverse"]], "size() (multidigraph method)": [[967, "networkx.MultiDiGraph.size"]], "subgraph() (multidigraph method)": [[968, "networkx.MultiDiGraph.subgraph"]], "succ (multidigraph property)": [[969, "networkx.MultiDiGraph.succ"]], "successors() (multidigraph method)": [[970, "networkx.MultiDiGraph.successors"]], "to_directed() (multidigraph method)": [[971, "networkx.MultiDiGraph.to_directed"]], "to_undirected() (multidigraph method)": [[972, "networkx.MultiDiGraph.to_undirected"]], "update() (multidigraph method)": [[973, "networkx.MultiDiGraph.update"]], "__contains__() (multigraph method)": [[974, "networkx.MultiGraph.__contains__"]], "__getitem__() (multigraph method)": [[975, "networkx.MultiGraph.__getitem__"]], "__init__() (multigraph method)": [[976, "networkx.MultiGraph.__init__"]], "__iter__() (multigraph method)": [[977, "networkx.MultiGraph.__iter__"]], "__len__() (multigraph method)": [[978, "networkx.MultiGraph.__len__"]], "add_edge() (multigraph method)": [[979, "networkx.MultiGraph.add_edge"]], "add_edges_from() (multigraph method)": [[980, "networkx.MultiGraph.add_edges_from"]], "add_node() (multigraph method)": [[981, "networkx.MultiGraph.add_node"]], "add_nodes_from() (multigraph method)": [[982, "networkx.MultiGraph.add_nodes_from"]], "add_weighted_edges_from() (multigraph method)": [[983, "networkx.MultiGraph.add_weighted_edges_from"]], "adj (multigraph property)": [[984, "networkx.MultiGraph.adj"]], "adjacency() (multigraph method)": [[985, "networkx.MultiGraph.adjacency"]], "clear() (multigraph method)": [[986, "networkx.MultiGraph.clear"]], "clear_edges() (multigraph method)": [[987, "networkx.MultiGraph.clear_edges"]], "copy() (multigraph method)": [[988, "networkx.MultiGraph.copy"]], "degree (multigraph property)": [[989, "networkx.MultiGraph.degree"]], "edge_subgraph() (multigraph method)": [[990, "networkx.MultiGraph.edge_subgraph"]], "edges (multigraph property)": [[991, "networkx.MultiGraph.edges"]], "get_edge_data() (multigraph method)": [[992, "networkx.MultiGraph.get_edge_data"]], "has_edge() (multigraph method)": [[993, "networkx.MultiGraph.has_edge"]], "has_node() (multigraph method)": [[994, "networkx.MultiGraph.has_node"]], "nbunch_iter() (multigraph method)": [[995, "networkx.MultiGraph.nbunch_iter"]], "neighbors() (multigraph method)": [[996, "networkx.MultiGraph.neighbors"]], "new_edge_key() (multigraph method)": [[997, "networkx.MultiGraph.new_edge_key"]], "nodes (multigraph property)": [[998, "networkx.MultiGraph.nodes"]], "number_of_edges() (multigraph method)": [[999, "networkx.MultiGraph.number_of_edges"]], "number_of_nodes() (multigraph method)": [[1000, "networkx.MultiGraph.number_of_nodes"]], "order() (multigraph method)": [[1001, "networkx.MultiGraph.order"]], "remove_edge() (multigraph method)": [[1002, "networkx.MultiGraph.remove_edge"]], "remove_edges_from() (multigraph method)": [[1003, "networkx.MultiGraph.remove_edges_from"]], "remove_node() (multigraph method)": [[1004, "networkx.MultiGraph.remove_node"]], "remove_nodes_from() (multigraph method)": [[1005, "networkx.MultiGraph.remove_nodes_from"]], "size() (multigraph method)": [[1006, "networkx.MultiGraph.size"]], "subgraph() (multigraph method)": [[1007, "networkx.MultiGraph.subgraph"]], "to_directed() (multigraph method)": [[1008, "networkx.MultiGraph.to_directed"]], "to_undirected() (multigraph method)": [[1009, "networkx.MultiGraph.to_undirected"]], "update() (multigraph method)": [[1010, "networkx.MultiGraph.update"]], "_dispatch() (in module networkx.classes.backends)": [[1011, "networkx.classes.backends._dispatch"]], "adjacencyview (class in networkx.classes.coreviews)": [[1012, "networkx.classes.coreviews.AdjacencyView"]], "__init__() (adjacencyview method)": [[1012, "networkx.classes.coreviews.AdjacencyView.__init__"]], "atlasview (class in networkx.classes.coreviews)": [[1013, "networkx.classes.coreviews.AtlasView"]], "__init__() (atlasview method)": [[1013, "networkx.classes.coreviews.AtlasView.__init__"]], "filteradjacency (class in networkx.classes.coreviews)": [[1014, "networkx.classes.coreviews.FilterAdjacency"]], "__init__() (filteradjacency method)": [[1014, "networkx.classes.coreviews.FilterAdjacency.__init__"]], "filteratlas (class in networkx.classes.coreviews)": [[1015, "networkx.classes.coreviews.FilterAtlas"]], "__init__() (filteratlas method)": [[1015, "networkx.classes.coreviews.FilterAtlas.__init__"]], "filtermultiadjacency (class in networkx.classes.coreviews)": [[1016, "networkx.classes.coreviews.FilterMultiAdjacency"]], "__init__() (filtermultiadjacency method)": [[1016, "networkx.classes.coreviews.FilterMultiAdjacency.__init__"]], "filtermultiinner (class in networkx.classes.coreviews)": [[1017, "networkx.classes.coreviews.FilterMultiInner"]], "__init__() (filtermultiinner method)": [[1017, "networkx.classes.coreviews.FilterMultiInner.__init__"]], "multiadjacencyview (class in networkx.classes.coreviews)": [[1018, "networkx.classes.coreviews.MultiAdjacencyView"]], "__init__() (multiadjacencyview method)": [[1018, "networkx.classes.coreviews.MultiAdjacencyView.__init__"]], "unionadjacency (class in networkx.classes.coreviews)": [[1019, "networkx.classes.coreviews.UnionAdjacency"]], "__init__() (unionadjacency method)": [[1019, "networkx.classes.coreviews.UnionAdjacency.__init__"]], "unionatlas (class in networkx.classes.coreviews)": [[1020, "networkx.classes.coreviews.UnionAtlas"]], "__init__() (unionatlas method)": [[1020, "networkx.classes.coreviews.UnionAtlas.__init__"]], "unionmultiadjacency (class in networkx.classes.coreviews)": [[1021, "networkx.classes.coreviews.UnionMultiAdjacency"]], "__init__() (unionmultiadjacency method)": [[1021, "networkx.classes.coreviews.UnionMultiAdjacency.__init__"]], "unionmultiinner (class in networkx.classes.coreviews)": [[1022, "networkx.classes.coreviews.UnionMultiInner"]], "__init__() (unionmultiinner method)": [[1022, "networkx.classes.coreviews.UnionMultiInner.__init__"]], "hide_diedges() (in module networkx.classes.filters)": [[1023, "networkx.classes.filters.hide_diedges"]], "hide_edges() (in module networkx.classes.filters)": [[1024, "networkx.classes.filters.hide_edges"]], "hide_multidiedges() (in module networkx.classes.filters)": [[1025, "networkx.classes.filters.hide_multidiedges"]], "hide_multiedges() (in module networkx.classes.filters)": [[1026, "networkx.classes.filters.hide_multiedges"]], "hide_nodes() (in module networkx.classes.filters)": [[1027, "networkx.classes.filters.hide_nodes"]], "no_filter() (in module networkx.classes.filters)": [[1028, "networkx.classes.filters.no_filter"]], "show_diedges() (in module networkx.classes.filters)": [[1029, "networkx.classes.filters.show_diedges"]], "show_edges() (in module networkx.classes.filters)": [[1030, "networkx.classes.filters.show_edges"]], "show_multidiedges() (in module networkx.classes.filters)": [[1031, "networkx.classes.filters.show_multidiedges"]], "show_multiedges() (in module networkx.classes.filters)": [[1032, "networkx.classes.filters.show_multiedges"]], "__init__() (show_nodes method)": [[1033, "networkx.classes.filters.show_nodes.__init__"]], "show_nodes (class in networkx.classes.filters)": [[1033, "networkx.classes.filters.show_nodes"]], "generic_graph_view() (in module networkx.classes.graphviews)": [[1034, "networkx.classes.graphviews.generic_graph_view"]], "reverse_view() (in module networkx.classes.graphviews)": [[1035, "networkx.classes.graphviews.reverse_view"]], "subgraph_view() (in module networkx.classes.graphviews)": [[1036, "networkx.classes.graphviews.subgraph_view"]], "graph (class in networkx)": [[1037, "networkx.Graph"]], "networkx.classes.backends": [[1038, "module-networkx.classes.backends"]], "networkx.classes.coreviews": [[1038, "module-networkx.classes.coreviews"]], "networkx.classes.filters": [[1038, "module-networkx.classes.filters"]], "networkx.classes.graphviews": [[1038, "module-networkx.classes.graphviews"]], "multidigraph (class in networkx)": [[1039, "networkx.MultiDiGraph"]], "multigraph (class in networkx)": [[1040, "networkx.MultiGraph"]], "networkx.convert": [[1041, "module-networkx.convert"]], "networkx.convert_matrix": [[1041, "module-networkx.convert_matrix"]], "networkx.drawing.layout": [[1042, "module-networkx.drawing.layout"]], "networkx.drawing.nx_agraph": [[1042, "module-networkx.drawing.nx_agraph"]], "networkx.drawing.nx_pydot": [[1042, "module-networkx.drawing.nx_pydot"]], "networkx.drawing.nx_pylab": [[1042, "module-networkx.drawing.nx_pylab"]], "ambiguoussolution (class in networkx)": [[1043, "networkx.AmbiguousSolution"]], "exceededmaxiterations (class in networkx)": [[1043, "networkx.ExceededMaxIterations"]], "hasacycle (class in networkx)": [[1043, "networkx.HasACycle"]], "networkxalgorithmerror (class in networkx)": [[1043, "networkx.NetworkXAlgorithmError"]], "networkxerror (class in networkx)": [[1043, "networkx.NetworkXError"]], "networkxexception (class in networkx)": [[1043, "networkx.NetworkXException"]], "networkxnocycle (class in networkx)": [[1043, "networkx.NetworkXNoCycle"]], "networkxnopath (class in networkx)": [[1043, "networkx.NetworkXNoPath"]], "networkxnotimplemented (class in networkx)": [[1043, "networkx.NetworkXNotImplemented"]], "networkxpointlessconcept (class in networkx)": [[1043, "networkx.NetworkXPointlessConcept"]], "networkxunbounded (class in networkx)": [[1043, "networkx.NetworkXUnbounded"]], "networkxunfeasible (class in networkx)": [[1043, "networkx.NetworkXUnfeasible"]], "nodenotfound (class in networkx)": [[1043, "networkx.NodeNotFound"]], "poweriterationfailedconvergence (class in networkx)": [[1043, "networkx.PowerIterationFailedConvergence"]], "networkx.exception": [[1043, "module-networkx.exception"]], "networkx.classes.function": [[1044, "module-networkx.classes.function"]], "assemble() (argmap method)": [[1045, "networkx.utils.decorators.argmap.assemble"]], "compile() (argmap method)": [[1046, "networkx.utils.decorators.argmap.compile"]], "signature() (argmap class method)": [[1047, "networkx.utils.decorators.argmap.signature"]], "pop() (mappedqueue method)": [[1048, "networkx.utils.mapped_queue.MappedQueue.pop"]], "push() (mappedqueue method)": [[1049, "networkx.utils.mapped_queue.MappedQueue.push"]], "remove() (mappedqueue method)": [[1050, "networkx.utils.mapped_queue.MappedQueue.remove"]], "update() (mappedqueue method)": [[1051, "networkx.utils.mapped_queue.MappedQueue.update"]], "add_cycle() (in module networkx.classes.function)": [[1052, "networkx.classes.function.add_cycle"]], "add_path() (in module networkx.classes.function)": [[1053, "networkx.classes.function.add_path"]], "add_star() (in module networkx.classes.function)": [[1054, "networkx.classes.function.add_star"]], "all_neighbors() (in module networkx.classes.function)": [[1055, "networkx.classes.function.all_neighbors"]], "common_neighbors() (in module networkx.classes.function)": [[1056, "networkx.classes.function.common_neighbors"]], "create_empty_copy() (in module networkx.classes.function)": [[1057, "networkx.classes.function.create_empty_copy"]], "degree() (in module networkx.classes.function)": [[1058, "networkx.classes.function.degree"]], "degree_histogram() (in module networkx.classes.function)": [[1059, "networkx.classes.function.degree_histogram"]], "density() (in module networkx.classes.function)": [[1060, "networkx.classes.function.density"]], "edge_subgraph() (in module networkx.classes.function)": [[1061, "networkx.classes.function.edge_subgraph"]], "edges() (in module networkx.classes.function)": [[1062, "networkx.classes.function.edges"]], "freeze() (in module networkx.classes.function)": [[1063, "networkx.classes.function.freeze"]], "get_edge_attributes() (in module networkx.classes.function)": [[1064, "networkx.classes.function.get_edge_attributes"]], "get_node_attributes() (in module networkx.classes.function)": [[1065, "networkx.classes.function.get_node_attributes"]], "induced_subgraph() (in module networkx.classes.function)": [[1066, "networkx.classes.function.induced_subgraph"]], "is_directed() (in module networkx.classes.function)": [[1067, "networkx.classes.function.is_directed"]], "is_empty() (in module networkx.classes.function)": [[1068, "networkx.classes.function.is_empty"]], "is_frozen() (in module networkx.classes.function)": [[1069, "networkx.classes.function.is_frozen"]], "is_negatively_weighted() (in module networkx.classes.function)": [[1070, "networkx.classes.function.is_negatively_weighted"]], "is_path() (in module networkx.classes.function)": [[1071, "networkx.classes.function.is_path"]], "is_weighted() (in module networkx.classes.function)": [[1072, "networkx.classes.function.is_weighted"]], "neighbors() (in module networkx.classes.function)": [[1073, "networkx.classes.function.neighbors"]], "nodes() (in module networkx.classes.function)": [[1074, "networkx.classes.function.nodes"]], "nodes_with_selfloops() (in module networkx.classes.function)": [[1075, "networkx.classes.function.nodes_with_selfloops"]], "non_edges() (in module networkx.classes.function)": [[1076, "networkx.classes.function.non_edges"]], "non_neighbors() (in module networkx.classes.function)": [[1077, "networkx.classes.function.non_neighbors"]], "number_of_edges() (in module networkx.classes.function)": [[1078, "networkx.classes.function.number_of_edges"]], "number_of_nodes() (in module networkx.classes.function)": [[1079, "networkx.classes.function.number_of_nodes"]], "number_of_selfloops() (in module networkx.classes.function)": [[1080, "networkx.classes.function.number_of_selfloops"]], "path_weight() (in module networkx.classes.function)": [[1081, "networkx.classes.function.path_weight"]], "restricted_view() (in module networkx.classes.function)": [[1082, "networkx.classes.function.restricted_view"]], "reverse_view() (in module networkx.classes.function)": [[1083, "networkx.classes.function.reverse_view"]], "selfloop_edges() (in module networkx.classes.function)": [[1084, "networkx.classes.function.selfloop_edges"]], "set_edge_attributes() (in module networkx.classes.function)": [[1085, "networkx.classes.function.set_edge_attributes"]], "set_node_attributes() (in module networkx.classes.function)": [[1086, "networkx.classes.function.set_node_attributes"]], "subgraph() (in module networkx.classes.function)": [[1087, "networkx.classes.function.subgraph"]], "subgraph_view() (in module networkx.classes.function)": [[1088, "networkx.classes.function.subgraph_view"]], "to_directed() (in module networkx.classes.function)": [[1089, "networkx.classes.function.to_directed"]], "to_undirected() (in module networkx.classes.function)": [[1090, "networkx.classes.function.to_undirected"]], "from_dict_of_dicts() (in module networkx.convert)": [[1091, "networkx.convert.from_dict_of_dicts"]], "from_dict_of_lists() (in module networkx.convert)": [[1092, "networkx.convert.from_dict_of_lists"]], "from_edgelist() (in module networkx.convert)": [[1093, "networkx.convert.from_edgelist"]], "to_dict_of_dicts() (in module networkx.convert)": [[1094, "networkx.convert.to_dict_of_dicts"]], "to_dict_of_lists() (in module networkx.convert)": [[1095, "networkx.convert.to_dict_of_lists"]], "to_edgelist() (in module networkx.convert)": [[1096, "networkx.convert.to_edgelist"]], "to_networkx_graph() (in module networkx.convert)": [[1097, "networkx.convert.to_networkx_graph"]], "from_numpy_array() (in module networkx.convert_matrix)": [[1098, "networkx.convert_matrix.from_numpy_array"]], "from_pandas_adjacency() (in module networkx.convert_matrix)": [[1099, "networkx.convert_matrix.from_pandas_adjacency"]], "from_pandas_edgelist() (in module networkx.convert_matrix)": [[1100, "networkx.convert_matrix.from_pandas_edgelist"]], "from_scipy_sparse_array() (in module networkx.convert_matrix)": [[1101, "networkx.convert_matrix.from_scipy_sparse_array"]], "to_numpy_array() (in module networkx.convert_matrix)": [[1102, "networkx.convert_matrix.to_numpy_array"]], "to_pandas_adjacency() (in module networkx.convert_matrix)": [[1103, "networkx.convert_matrix.to_pandas_adjacency"]], "to_pandas_edgelist() (in module networkx.convert_matrix)": [[1104, "networkx.convert_matrix.to_pandas_edgelist"]], "to_scipy_sparse_array() (in module networkx.convert_matrix)": [[1105, "networkx.convert_matrix.to_scipy_sparse_array"]], "bipartite_layout() (in module networkx.drawing.layout)": [[1106, "networkx.drawing.layout.bipartite_layout"]], "circular_layout() (in module networkx.drawing.layout)": [[1107, "networkx.drawing.layout.circular_layout"]], "kamada_kawai_layout() (in module networkx.drawing.layout)": [[1108, "networkx.drawing.layout.kamada_kawai_layout"]], "multipartite_layout() (in module networkx.drawing.layout)": [[1109, "networkx.drawing.layout.multipartite_layout"]], "planar_layout() (in module networkx.drawing.layout)": [[1110, "networkx.drawing.layout.planar_layout"]], "random_layout() (in module networkx.drawing.layout)": [[1111, "networkx.drawing.layout.random_layout"]], "rescale_layout() (in module networkx.drawing.layout)": [[1112, "networkx.drawing.layout.rescale_layout"]], "rescale_layout_dict() (in module networkx.drawing.layout)": [[1113, "networkx.drawing.layout.rescale_layout_dict"]], "shell_layout() (in module networkx.drawing.layout)": [[1114, "networkx.drawing.layout.shell_layout"]], "spectral_layout() (in module networkx.drawing.layout)": [[1115, "networkx.drawing.layout.spectral_layout"]], "spiral_layout() (in module networkx.drawing.layout)": [[1116, "networkx.drawing.layout.spiral_layout"]], "spring_layout() (in module networkx.drawing.layout)": [[1117, "networkx.drawing.layout.spring_layout"]], "from_agraph() (in module networkx.drawing.nx_agraph)": [[1118, "networkx.drawing.nx_agraph.from_agraph"]], "graphviz_layout() (in module networkx.drawing.nx_agraph)": [[1119, "networkx.drawing.nx_agraph.graphviz_layout"]], "pygraphviz_layout() (in module networkx.drawing.nx_agraph)": [[1120, "networkx.drawing.nx_agraph.pygraphviz_layout"]], "read_dot() (in module networkx.drawing.nx_agraph)": [[1121, "networkx.drawing.nx_agraph.read_dot"]], "to_agraph() (in module networkx.drawing.nx_agraph)": [[1122, "networkx.drawing.nx_agraph.to_agraph"]], "write_dot() (in module networkx.drawing.nx_agraph)": [[1123, "networkx.drawing.nx_agraph.write_dot"]], "from_pydot() (in module networkx.drawing.nx_pydot)": [[1124, "networkx.drawing.nx_pydot.from_pydot"]], "graphviz_layout() (in module networkx.drawing.nx_pydot)": [[1125, "networkx.drawing.nx_pydot.graphviz_layout"]], "pydot_layout() (in module networkx.drawing.nx_pydot)": [[1126, "networkx.drawing.nx_pydot.pydot_layout"]], "read_dot() (in module networkx.drawing.nx_pydot)": [[1127, "networkx.drawing.nx_pydot.read_dot"]], "to_pydot() (in module networkx.drawing.nx_pydot)": [[1128, "networkx.drawing.nx_pydot.to_pydot"]], "write_dot() (in module networkx.drawing.nx_pydot)": [[1129, "networkx.drawing.nx_pydot.write_dot"]], "draw() (in module networkx.drawing.nx_pylab)": [[1130, "networkx.drawing.nx_pylab.draw"]], "draw_circular() (in module networkx.drawing.nx_pylab)": [[1131, "networkx.drawing.nx_pylab.draw_circular"]], "draw_kamada_kawai() (in module networkx.drawing.nx_pylab)": [[1132, "networkx.drawing.nx_pylab.draw_kamada_kawai"]], "draw_networkx() (in module networkx.drawing.nx_pylab)": [[1133, "networkx.drawing.nx_pylab.draw_networkx"]], "draw_networkx_edge_labels() (in module networkx.drawing.nx_pylab)": [[1134, "networkx.drawing.nx_pylab.draw_networkx_edge_labels"]], "draw_networkx_edges() (in module networkx.drawing.nx_pylab)": [[1135, "networkx.drawing.nx_pylab.draw_networkx_edges"]], "draw_networkx_labels() (in module networkx.drawing.nx_pylab)": [[1136, "networkx.drawing.nx_pylab.draw_networkx_labels"]], "draw_networkx_nodes() (in module networkx.drawing.nx_pylab)": [[1137, "networkx.drawing.nx_pylab.draw_networkx_nodes"]], "draw_planar() (in module networkx.drawing.nx_pylab)": [[1138, "networkx.drawing.nx_pylab.draw_planar"]], "draw_random() (in module networkx.drawing.nx_pylab)": [[1139, "networkx.drawing.nx_pylab.draw_random"]], "draw_shell() (in module networkx.drawing.nx_pylab)": [[1140, "networkx.drawing.nx_pylab.draw_shell"]], "draw_spectral() (in module networkx.drawing.nx_pylab)": [[1141, "networkx.drawing.nx_pylab.draw_spectral"]], "draw_spring() (in module networkx.drawing.nx_pylab)": [[1142, "networkx.drawing.nx_pylab.draw_spring"]], "graph_atlas() (in module networkx.generators.atlas)": [[1143, "networkx.generators.atlas.graph_atlas"]], "graph_atlas_g() (in module networkx.generators.atlas)": [[1144, "networkx.generators.atlas.graph_atlas_g"]], "balanced_tree() (in module networkx.generators.classic)": [[1145, "networkx.generators.classic.balanced_tree"]], "barbell_graph() (in module networkx.generators.classic)": [[1146, "networkx.generators.classic.barbell_graph"]], "binomial_tree() (in module networkx.generators.classic)": [[1147, "networkx.generators.classic.binomial_tree"]], "circulant_graph() (in module networkx.generators.classic)": [[1148, "networkx.generators.classic.circulant_graph"]], "circular_ladder_graph() (in module networkx.generators.classic)": [[1149, "networkx.generators.classic.circular_ladder_graph"]], "complete_graph() (in module networkx.generators.classic)": [[1150, "networkx.generators.classic.complete_graph"]], "complete_multipartite_graph() (in module networkx.generators.classic)": [[1151, "networkx.generators.classic.complete_multipartite_graph"]], "cycle_graph() (in module networkx.generators.classic)": [[1152, 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module networkx.generators.classic)": [[1161, "networkx.generators.classic.trivial_graph"]], "turan_graph() (in module networkx.generators.classic)": [[1162, "networkx.generators.classic.turan_graph"]], "wheel_graph() (in module networkx.generators.classic)": [[1163, "networkx.generators.classic.wheel_graph"]], "random_cograph() (in module networkx.generators.cographs)": [[1164, "networkx.generators.cographs.random_cograph"]], "lfr_benchmark_graph() (in module networkx.generators.community)": [[1165, "networkx.generators.community.LFR_benchmark_graph"]], "caveman_graph() (in module networkx.generators.community)": [[1166, "networkx.generators.community.caveman_graph"]], "connected_caveman_graph() (in module networkx.generators.community)": [[1167, "networkx.generators.community.connected_caveman_graph"]], "gaussian_random_partition_graph() (in module networkx.generators.community)": [[1168, "networkx.generators.community.gaussian_random_partition_graph"]], "planted_partition_graph() 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"networkx.generators.degree_seq.degree_sequence_tree"]], "directed_configuration_model() (in module networkx.generators.degree_seq)": [[1177, "networkx.generators.degree_seq.directed_configuration_model"]], "directed_havel_hakimi_graph() (in module networkx.generators.degree_seq)": [[1178, "networkx.generators.degree_seq.directed_havel_hakimi_graph"]], "expected_degree_graph() (in module networkx.generators.degree_seq)": [[1179, "networkx.generators.degree_seq.expected_degree_graph"]], "havel_hakimi_graph() (in module networkx.generators.degree_seq)": [[1180, "networkx.generators.degree_seq.havel_hakimi_graph"]], "random_degree_sequence_graph() (in module networkx.generators.degree_seq)": [[1181, "networkx.generators.degree_seq.random_degree_sequence_graph"]], "gn_graph() (in module networkx.generators.directed)": [[1182, "networkx.generators.directed.gn_graph"]], "gnc_graph() (in module networkx.generators.directed)": [[1183, "networkx.generators.directed.gnc_graph"]], "gnr_graph() 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networkx.generators.intersection)": [[1205, "networkx.generators.intersection.uniform_random_intersection_graph"]], "interval_graph() (in module networkx.generators.interval_graph)": [[1206, "networkx.generators.interval_graph.interval_graph"]], "directed_joint_degree_graph() (in module networkx.generators.joint_degree_seq)": [[1207, "networkx.generators.joint_degree_seq.directed_joint_degree_graph"]], "is_valid_directed_joint_degree() (in module networkx.generators.joint_degree_seq)": [[1208, "networkx.generators.joint_degree_seq.is_valid_directed_joint_degree"]], "is_valid_joint_degree() (in module networkx.generators.joint_degree_seq)": [[1209, "networkx.generators.joint_degree_seq.is_valid_joint_degree"]], "joint_degree_graph() (in module networkx.generators.joint_degree_seq)": [[1210, "networkx.generators.joint_degree_seq.joint_degree_graph"]], "grid_2d_graph() (in module networkx.generators.lattice)": [[1211, "networkx.generators.lattice.grid_2d_graph"]], "grid_graph() (in module networkx.generators.lattice)": [[1212, "networkx.generators.lattice.grid_graph"]], "hexagonal_lattice_graph() (in module networkx.generators.lattice)": [[1213, "networkx.generators.lattice.hexagonal_lattice_graph"]], "hypercube_graph() (in module networkx.generators.lattice)": [[1214, "networkx.generators.lattice.hypercube_graph"]], "triangular_lattice_graph() (in module networkx.generators.lattice)": [[1215, "networkx.generators.lattice.triangular_lattice_graph"]], "inverse_line_graph() (in module networkx.generators.line)": [[1216, "networkx.generators.line.inverse_line_graph"]], "line_graph() (in module networkx.generators.line)": [[1217, "networkx.generators.line.line_graph"]], "mycielski_graph() (in module networkx.generators.mycielski)": [[1218, "networkx.generators.mycielski.mycielski_graph"]], "mycielskian() (in module networkx.generators.mycielski)": [[1219, "networkx.generators.mycielski.mycielskian"]], "nonisomorphic_trees() (in module networkx.generators.nonisomorphic_trees)": [[1220, "networkx.generators.nonisomorphic_trees.nonisomorphic_trees"]], "number_of_nonisomorphic_trees() (in module networkx.generators.nonisomorphic_trees)": [[1221, "networkx.generators.nonisomorphic_trees.number_of_nonisomorphic_trees"]], "random_clustered_graph() (in module networkx.generators.random_clustered)": [[1222, "networkx.generators.random_clustered.random_clustered_graph"]], "barabasi_albert_graph() (in module networkx.generators.random_graphs)": [[1223, "networkx.generators.random_graphs.barabasi_albert_graph"]], "binomial_graph() (in module networkx.generators.random_graphs)": [[1224, "networkx.generators.random_graphs.binomial_graph"]], "connected_watts_strogatz_graph() (in module networkx.generators.random_graphs)": [[1225, "networkx.generators.random_graphs.connected_watts_strogatz_graph"]], "dense_gnm_random_graph() (in module networkx.generators.random_graphs)": [[1226, 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"networkx.generators.random_graphs.random_shell_graph"]], "watts_strogatz_graph() (in module networkx.generators.random_graphs)": [[1241, "networkx.generators.random_graphs.watts_strogatz_graph"]], "lcf_graph() (in module networkx.generators.small)": [[1242, "networkx.generators.small.LCF_graph"]], "bull_graph() (in module networkx.generators.small)": [[1243, "networkx.generators.small.bull_graph"]], "chvatal_graph() (in module networkx.generators.small)": [[1244, "networkx.generators.small.chvatal_graph"]], "cubical_graph() (in module networkx.generators.small)": [[1245, "networkx.generators.small.cubical_graph"]], "desargues_graph() (in module networkx.generators.small)": [[1246, "networkx.generators.small.desargues_graph"]], "diamond_graph() (in module networkx.generators.small)": [[1247, "networkx.generators.small.diamond_graph"]], "dodecahedral_graph() (in module networkx.generators.small)": [[1248, "networkx.generators.small.dodecahedral_graph"]], "frucht_graph() (in module networkx.generators.small)": [[1249, "networkx.generators.small.frucht_graph"]], "heawood_graph() (in module networkx.generators.small)": [[1250, "networkx.generators.small.heawood_graph"]], "hoffman_singleton_graph() (in module networkx.generators.small)": [[1251, "networkx.generators.small.hoffman_singleton_graph"]], "house_graph() (in module networkx.generators.small)": [[1252, "networkx.generators.small.house_graph"]], "house_x_graph() (in module networkx.generators.small)": [[1253, "networkx.generators.small.house_x_graph"]], "icosahedral_graph() (in module networkx.generators.small)": [[1254, "networkx.generators.small.icosahedral_graph"]], "krackhardt_kite_graph() (in module networkx.generators.small)": [[1255, "networkx.generators.small.krackhardt_kite_graph"]], "moebius_kantor_graph() (in module networkx.generators.small)": [[1256, "networkx.generators.small.moebius_kantor_graph"]], "octahedral_graph() (in module networkx.generators.small)": [[1257, 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"networkx.generators.trees.random_tree"]], "triad_graph() (in module networkx.generators.triads)": [[1274, "networkx.generators.triads.triad_graph"]], "algebraic_connectivity() (in module networkx.linalg.algebraicconnectivity)": [[1275, "networkx.linalg.algebraicconnectivity.algebraic_connectivity"]], "fiedler_vector() (in module networkx.linalg.algebraicconnectivity)": [[1276, "networkx.linalg.algebraicconnectivity.fiedler_vector"]], "spectral_ordering() (in module networkx.linalg.algebraicconnectivity)": [[1277, "networkx.linalg.algebraicconnectivity.spectral_ordering"]], "attr_matrix() (in module networkx.linalg.attrmatrix)": [[1278, "networkx.linalg.attrmatrix.attr_matrix"]], "attr_sparse_matrix() (in module networkx.linalg.attrmatrix)": [[1279, "networkx.linalg.attrmatrix.attr_sparse_matrix"]], "bethe_hessian_matrix() (in module networkx.linalg.bethehessianmatrix)": [[1280, "networkx.linalg.bethehessianmatrix.bethe_hessian_matrix"]], "adjacency_matrix() (in module networkx.linalg.graphmatrix)": [[1281, "networkx.linalg.graphmatrix.adjacency_matrix"]], "incidence_matrix() (in module networkx.linalg.graphmatrix)": [[1282, "networkx.linalg.graphmatrix.incidence_matrix"]], "directed_combinatorial_laplacian_matrix() (in module networkx.linalg.laplacianmatrix)": [[1283, "networkx.linalg.laplacianmatrix.directed_combinatorial_laplacian_matrix"]], "directed_laplacian_matrix() (in module networkx.linalg.laplacianmatrix)": [[1284, "networkx.linalg.laplacianmatrix.directed_laplacian_matrix"]], "laplacian_matrix() (in module networkx.linalg.laplacianmatrix)": [[1285, "networkx.linalg.laplacianmatrix.laplacian_matrix"]], "normalized_laplacian_matrix() (in module networkx.linalg.laplacianmatrix)": [[1286, "networkx.linalg.laplacianmatrix.normalized_laplacian_matrix"]], "directed_modularity_matrix() (in module networkx.linalg.modularitymatrix)": [[1287, "networkx.linalg.modularitymatrix.directed_modularity_matrix"]], "modularity_matrix() (in module networkx.linalg.modularitymatrix)": [[1288, "networkx.linalg.modularitymatrix.modularity_matrix"]], "adjacency_spectrum() (in module networkx.linalg.spectrum)": [[1289, "networkx.linalg.spectrum.adjacency_spectrum"]], "bethe_hessian_spectrum() (in module networkx.linalg.spectrum)": [[1290, "networkx.linalg.spectrum.bethe_hessian_spectrum"]], "laplacian_spectrum() (in module networkx.linalg.spectrum)": [[1291, "networkx.linalg.spectrum.laplacian_spectrum"]], "modularity_spectrum() (in module networkx.linalg.spectrum)": [[1292, "networkx.linalg.spectrum.modularity_spectrum"]], "normalized_laplacian_spectrum() (in module networkx.linalg.spectrum)": [[1293, "networkx.linalg.spectrum.normalized_laplacian_spectrum"]], "convert_node_labels_to_integers() (in module networkx.relabel)": [[1294, "networkx.relabel.convert_node_labels_to_integers"]], "relabel_nodes() (in module networkx.relabel)": [[1295, "networkx.relabel.relabel_nodes"]], "__init__() (argmap method)": [[1296, "networkx.utils.decorators.argmap.__init__"]], "argmap (class in networkx.utils.decorators)": [[1296, "networkx.utils.decorators.argmap"]], "nodes_or_number() (in module networkx.utils.decorators)": [[1297, "networkx.utils.decorators.nodes_or_number"]], "not_implemented_for() (in module networkx.utils.decorators)": [[1298, "networkx.utils.decorators.not_implemented_for"]], "np_random_state() (in module networkx.utils.decorators)": [[1299, "networkx.utils.decorators.np_random_state"]], "open_file() (in module networkx.utils.decorators)": [[1300, "networkx.utils.decorators.open_file"]], "py_random_state() (in module networkx.utils.decorators)": [[1301, "networkx.utils.decorators.py_random_state"]], "mappedqueue (class in networkx.utils.mapped_queue)": [[1302, "networkx.utils.mapped_queue.MappedQueue"]], "__init__() (mappedqueue method)": [[1302, "networkx.utils.mapped_queue.MappedQueue.__init__"]], "arbitrary_element() (in module networkx.utils.misc)": [[1303, "networkx.utils.misc.arbitrary_element"]], "create_py_random_state() (in module networkx.utils.misc)": [[1304, "networkx.utils.misc.create_py_random_state"]], "create_random_state() (in module networkx.utils.misc)": [[1305, "networkx.utils.misc.create_random_state"]], "dict_to_numpy_array() (in module networkx.utils.misc)": [[1306, "networkx.utils.misc.dict_to_numpy_array"]], "edges_equal() (in module networkx.utils.misc)": [[1307, "networkx.utils.misc.edges_equal"]], "flatten() (in module networkx.utils.misc)": [[1308, "networkx.utils.misc.flatten"]], "graphs_equal() (in module networkx.utils.misc)": [[1309, "networkx.utils.misc.graphs_equal"]], "groups() (in module networkx.utils.misc)": [[1310, "networkx.utils.misc.groups"]], "make_list_of_ints() (in module networkx.utils.misc)": [[1311, "networkx.utils.misc.make_list_of_ints"]], "nodes_equal() (in module networkx.utils.misc)": [[1312, "networkx.utils.misc.nodes_equal"]], "pairwise() (in module networkx.utils.misc)": [[1313, "networkx.utils.misc.pairwise"]], "cumulative_distribution() (in module networkx.utils.random_sequence)": [[1314, "networkx.utils.random_sequence.cumulative_distribution"]], "discrete_sequence() (in module networkx.utils.random_sequence)": [[1315, "networkx.utils.random_sequence.discrete_sequence"]], "powerlaw_sequence() (in module networkx.utils.random_sequence)": [[1316, "networkx.utils.random_sequence.powerlaw_sequence"]], "random_weighted_sample() (in module networkx.utils.random_sequence)": [[1317, "networkx.utils.random_sequence.random_weighted_sample"]], "weighted_choice() (in module networkx.utils.random_sequence)": [[1318, "networkx.utils.random_sequence.weighted_choice"]], "zipf_rv() (in module networkx.utils.random_sequence)": [[1319, "networkx.utils.random_sequence.zipf_rv"]], "cuthill_mckee_ordering() (in module networkx.utils.rcm)": [[1320, "networkx.utils.rcm.cuthill_mckee_ordering"]], "reverse_cuthill_mckee_ordering() (in module networkx.utils.rcm)": [[1321, "networkx.utils.rcm.reverse_cuthill_mckee_ordering"]], "union() (unionfind method)": [[1322, "networkx.utils.union_find.UnionFind.union"]], "networkx.generators.atlas": [[1323, "module-networkx.generators.atlas"]], "networkx.generators.classic": [[1323, "module-networkx.generators.classic"]], "networkx.generators.cographs": [[1323, "module-networkx.generators.cographs"]], "networkx.generators.community": [[1323, "module-networkx.generators.community"]], "networkx.generators.degree_seq": [[1323, "module-networkx.generators.degree_seq"]], "networkx.generators.directed": [[1323, "module-networkx.generators.directed"]], "networkx.generators.duplication": [[1323, "module-networkx.generators.duplication"]], "networkx.generators.ego": [[1323, "module-networkx.generators.ego"]], "networkx.generators.expanders": [[1323, "module-networkx.generators.expanders"]], "networkx.generators.geometric": [[1323, "module-networkx.generators.geometric"]], "networkx.generators.harary_graph": [[1323, "module-networkx.generators.harary_graph"]], "networkx.generators.internet_as_graphs": [[1323, "module-networkx.generators.internet_as_graphs"]], "networkx.generators.intersection": [[1323, "module-networkx.generators.intersection"]], "networkx.generators.interval_graph": [[1323, "module-networkx.generators.interval_graph"]], "networkx.generators.joint_degree_seq": [[1323, "module-networkx.generators.joint_degree_seq"]], "networkx.generators.lattice": [[1323, "module-networkx.generators.lattice"]], "networkx.generators.line": [[1323, "module-networkx.generators.line"]], "networkx.generators.mycielski": [[1323, "module-networkx.generators.mycielski"]], "networkx.generators.nonisomorphic_trees": [[1323, "module-networkx.generators.nonisomorphic_trees"]], "networkx.generators.random_clustered": [[1323, "module-networkx.generators.random_clustered"]], "networkx.generators.random_graphs": [[1323, "module-networkx.generators.random_graphs"]], "networkx.generators.small": [[1323, "module-networkx.generators.small"]], "networkx.generators.social": [[1323, "module-networkx.generators.social"]], "networkx.generators.spectral_graph_forge": [[1323, "module-networkx.generators.spectral_graph_forge"]], "networkx.generators.stochastic": [[1323, "module-networkx.generators.stochastic"]], "networkx.generators.sudoku": [[1323, "module-networkx.generators.sudoku"]], "networkx.generators.trees": [[1323, "module-networkx.generators.trees"]], "networkx.generators.triads": [[1323, "module-networkx.generators.triads"]], "dictionary": [[1324, "term-dictionary"]], "ebunch": [[1324, "term-ebunch"]], "edge": [[1324, "term-edge"]], "edge attribute": [[1324, "term-edge-attribute"]], "nbunch": [[1324, "term-nbunch"]], "node": [[1324, "term-node"]], "node attribute": [[1324, "term-node-attribute"]], "networkx.linalg.algebraicconnectivity": [[1327, "module-networkx.linalg.algebraicconnectivity"]], "networkx.linalg.attrmatrix": [[1327, "module-networkx.linalg.attrmatrix"]], "networkx.linalg.bethehessianmatrix": [[1327, "module-networkx.linalg.bethehessianmatrix"]], "networkx.linalg.graphmatrix": [[1327, "module-networkx.linalg.graphmatrix"]], "networkx.linalg.laplacianmatrix": [[1327, "module-networkx.linalg.laplacianmatrix"]], "networkx.linalg.modularitymatrix": [[1327, "module-networkx.linalg.modularitymatrix"]], "networkx.linalg.spectrum": [[1327, "module-networkx.linalg.spectrum"]], "networkx.readwrite.adjlist": [[1329, "module-networkx.readwrite.adjlist"]], "networkx.readwrite.edgelist": [[1330, "module-networkx.readwrite.edgelist"]], "generate_adjlist() (in module networkx.readwrite.adjlist)": [[1331, "networkx.readwrite.adjlist.generate_adjlist"]], "parse_adjlist() (in module networkx.readwrite.adjlist)": [[1332, "networkx.readwrite.adjlist.parse_adjlist"]], "read_adjlist() (in module networkx.readwrite.adjlist)": [[1333, "networkx.readwrite.adjlist.read_adjlist"]], "write_adjlist() (in module networkx.readwrite.adjlist)": [[1334, "networkx.readwrite.adjlist.write_adjlist"]], "generate_edgelist() (in module networkx.readwrite.edgelist)": [[1335, "networkx.readwrite.edgelist.generate_edgelist"]], "parse_edgelist() (in module networkx.readwrite.edgelist)": [[1336, "networkx.readwrite.edgelist.parse_edgelist"]], "read_edgelist() (in module networkx.readwrite.edgelist)": [[1337, "networkx.readwrite.edgelist.read_edgelist"]], "read_weighted_edgelist() (in module networkx.readwrite.edgelist)": [[1338, "networkx.readwrite.edgelist.read_weighted_edgelist"]], "write_edgelist() (in module networkx.readwrite.edgelist)": [[1339, "networkx.readwrite.edgelist.write_edgelist"]], "write_weighted_edgelist() (in module networkx.readwrite.edgelist)": [[1340, "networkx.readwrite.edgelist.write_weighted_edgelist"]], "generate_gexf() (in module networkx.readwrite.gexf)": [[1341, "networkx.readwrite.gexf.generate_gexf"]], "read_gexf() (in module networkx.readwrite.gexf)": [[1342, "networkx.readwrite.gexf.read_gexf"]], "relabel_gexf_graph() (in module networkx.readwrite.gexf)": [[1343, "networkx.readwrite.gexf.relabel_gexf_graph"]], "write_gexf() (in module networkx.readwrite.gexf)": [[1344, "networkx.readwrite.gexf.write_gexf"]], "generate_gml() (in module networkx.readwrite.gml)": [[1345, "networkx.readwrite.gml.generate_gml"]], "literal_destringizer() (in module networkx.readwrite.gml)": [[1346, "networkx.readwrite.gml.literal_destringizer"]], "literal_stringizer() (in module networkx.readwrite.gml)": [[1347, "networkx.readwrite.gml.literal_stringizer"]], "parse_gml() (in module networkx.readwrite.gml)": [[1348, "networkx.readwrite.gml.parse_gml"]], "read_gml() (in module networkx.readwrite.gml)": [[1349, "networkx.readwrite.gml.read_gml"]], "write_gml() (in module networkx.readwrite.gml)": [[1350, "networkx.readwrite.gml.write_gml"]], "from_graph6_bytes() (in module networkx.readwrite.graph6)": [[1351, "networkx.readwrite.graph6.from_graph6_bytes"]], "read_graph6() (in module networkx.readwrite.graph6)": [[1352, "networkx.readwrite.graph6.read_graph6"]], "to_graph6_bytes() (in module networkx.readwrite.graph6)": [[1353, "networkx.readwrite.graph6.to_graph6_bytes"]], "write_graph6() (in module networkx.readwrite.graph6)": [[1354, "networkx.readwrite.graph6.write_graph6"]], "generate_graphml() (in module networkx.readwrite.graphml)": [[1355, "networkx.readwrite.graphml.generate_graphml"]], "parse_graphml() (in module networkx.readwrite.graphml)": [[1356, "networkx.readwrite.graphml.parse_graphml"]], "read_graphml() (in module networkx.readwrite.graphml)": [[1357, "networkx.readwrite.graphml.read_graphml"]], "write_graphml() (in module networkx.readwrite.graphml)": [[1358, "networkx.readwrite.graphml.write_graphml"]], "adjacency_data() (in module networkx.readwrite.json_graph)": [[1359, "networkx.readwrite.json_graph.adjacency_data"]], "adjacency_graph() (in module networkx.readwrite.json_graph)": [[1360, "networkx.readwrite.json_graph.adjacency_graph"]], "cytoscape_data() (in module networkx.readwrite.json_graph)": [[1361, "networkx.readwrite.json_graph.cytoscape_data"]], "cytoscape_graph() (in module networkx.readwrite.json_graph)": [[1362, "networkx.readwrite.json_graph.cytoscape_graph"]], "node_link_data() (in module networkx.readwrite.json_graph)": [[1363, "networkx.readwrite.json_graph.node_link_data"]], "node_link_graph() (in module networkx.readwrite.json_graph)": [[1364, "networkx.readwrite.json_graph.node_link_graph"]], "tree_data() (in module networkx.readwrite.json_graph)": [[1365, "networkx.readwrite.json_graph.tree_data"]], "tree_graph() (in module networkx.readwrite.json_graph)": [[1366, "networkx.readwrite.json_graph.tree_graph"]], "parse_leda() (in module networkx.readwrite.leda)": [[1367, "networkx.readwrite.leda.parse_leda"]], "read_leda() (in module networkx.readwrite.leda)": [[1368, "networkx.readwrite.leda.read_leda"]], "generate_multiline_adjlist() (in module networkx.readwrite.multiline_adjlist)": [[1369, "networkx.readwrite.multiline_adjlist.generate_multiline_adjlist"]], "parse_multiline_adjlist() (in module networkx.readwrite.multiline_adjlist)": [[1370, "networkx.readwrite.multiline_adjlist.parse_multiline_adjlist"]], "read_multiline_adjlist() (in module networkx.readwrite.multiline_adjlist)": [[1371, "networkx.readwrite.multiline_adjlist.read_multiline_adjlist"]], "write_multiline_adjlist() (in module networkx.readwrite.multiline_adjlist)": [[1372, "networkx.readwrite.multiline_adjlist.write_multiline_adjlist"]], "generate_pajek() (in module networkx.readwrite.pajek)": [[1373, "networkx.readwrite.pajek.generate_pajek"]], "parse_pajek() (in module networkx.readwrite.pajek)": [[1374, "networkx.readwrite.pajek.parse_pajek"]], "read_pajek() (in module networkx.readwrite.pajek)": [[1375, "networkx.readwrite.pajek.read_pajek"]], "write_pajek() (in module networkx.readwrite.pajek)": [[1376, "networkx.readwrite.pajek.write_pajek"]], "from_sparse6_bytes() (in module networkx.readwrite.sparse6)": [[1377, "networkx.readwrite.sparse6.from_sparse6_bytes"]], "read_sparse6() (in module networkx.readwrite.sparse6)": [[1378, "networkx.readwrite.sparse6.read_sparse6"]], "to_sparse6_bytes() (in module networkx.readwrite.sparse6)": [[1379, "networkx.readwrite.sparse6.to_sparse6_bytes"]], "write_sparse6() (in module networkx.readwrite.sparse6)": [[1380, "networkx.readwrite.sparse6.write_sparse6"]], "networkx.readwrite.gexf": [[1381, "module-networkx.readwrite.gexf"]], "networkx.readwrite.gml": [[1382, "module-networkx.readwrite.gml"]], "networkx.readwrite.graphml": [[1383, "module-networkx.readwrite.graphml"]], "networkx.readwrite.json_graph": [[1385, "module-networkx.readwrite.json_graph"]], "networkx.readwrite.leda": [[1386, "module-networkx.readwrite.leda"]], "networkx.readwrite.multiline_adjlist": [[1388, "module-networkx.readwrite.multiline_adjlist"]], "networkx.readwrite.pajek": [[1389, "module-networkx.readwrite.pajek"]], "networkx.readwrite.graph6": [[1390, "module-networkx.readwrite.graph6"]], "networkx.readwrite.sparse6": [[1390, "module-networkx.readwrite.sparse6"]], "networkx.relabel": [[1391, "module-networkx.relabel"]], "networkx.utils": [[1392, "module-networkx.utils"]], "networkx.utils.decorators": [[1392, "module-networkx.utils.decorators"]], "networkx.utils.mapped_queue": [[1392, "module-networkx.utils.mapped_queue"]], "networkx.utils.misc": [[1392, "module-networkx.utils.misc"]], "networkx.utils.random_sequence": [[1392, "module-networkx.utils.random_sequence"]], "networkx.utils.rcm": [[1392, "module-networkx.utils.rcm"]], "networkx.utils.union_find": [[1392, "module-networkx.utils.union_find"]]}})
\ No newline at end of file diff --git a/tutorial-34.pdf b/tutorial-34.pdf Binary files differindex f96f5768..1fcafcd5 100644 --- a/tutorial-34.pdf +++ b/tutorial-34.pdf diff --git a/tutorial-35.pdf b/tutorial-35.pdf Binary files differindex 8bbcec21..f5571d06 100644 --- a/tutorial-35.pdf +++ b/tutorial-35.pdf diff --git a/tutorial-36.pdf b/tutorial-36.pdf Binary files differindex 5b8c01ab..ae5081bf 100644 --- a/tutorial-36.pdf +++ b/tutorial-36.pdf diff --git a/tutorial.ipynb b/tutorial.ipynb index 9c79f66d..b9afd1c2 100644 --- a/tutorial.ipynb +++ b/tutorial.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "e140aad2", + "id": "17631eeb", "metadata": {}, "source": [ "## Tutorial\n", @@ -17,7 +17,7 @@ { "cell_type": "code", "execution_count": null, - "id": "a76a2b61", + "id": "8b43999d", "metadata": {}, "outputs": [], "source": [ @@ -27,7 +27,7 @@ }, { "cell_type": "markdown", - "id": "16943313", + "id": "26ff4033", "metadata": {}, "source": [ "By definition, a `Graph` is a collection of nodes (vertices) along with\n", @@ -47,7 +47,7 @@ { "cell_type": "code", "execution_count": null, - "id": "61914daf", + "id": "0fe5988f", "metadata": {}, "outputs": [], "source": [ @@ -56,7 +56,7 @@ }, { "cell_type": "markdown", - "id": "6823de84", + "id": "222e2bae", "metadata": {}, "source": [ "or add nodes from any [iterable](https://docs.python.org/3/glossary.html#term-iterable) container, such as a list" @@ -65,7 +65,7 @@ { "cell_type": "code", "execution_count": null, - "id": "d1ce14dd", + "id": "57fe5cf9", "metadata": {}, "outputs": [], "source": [ @@ -74,7 +74,7 @@ }, { "cell_type": "markdown", - "id": "cf35f78c", + "id": "6df8a05b", "metadata": {}, "source": [ "You can also add nodes along with node\n", @@ -96,7 +96,7 @@ { "cell_type": "code", "execution_count": null, - "id": "0568aa4e", + "id": "289a49be", "metadata": {}, "outputs": [], "source": [ @@ -106,7 +106,7 @@ }, { "cell_type": "markdown", - "id": "80937cd6", + "id": "c0cfeabf", "metadata": {}, "source": [ "`G` now contains the nodes of `H` as nodes of `G`.\n", @@ -116,7 +116,7 @@ { "cell_type": "code", "execution_count": null, - "id": "662f7aea", + "id": "15930f02", "metadata": {}, "outputs": [], "source": [ @@ -125,7 +125,7 @@ }, { "cell_type": "markdown", - "id": "a07ca9e2", + "id": "4fe1f15d", "metadata": {}, "source": [ "The graph `G` now contains `H` as a node. This flexibility is very powerful as\n", @@ -143,7 +143,7 @@ { "cell_type": "code", "execution_count": null, - "id": "731da224", + "id": "8ec67ec0", "metadata": {}, "outputs": [], "source": [ @@ -154,7 +154,7 @@ }, { "cell_type": "markdown", - "id": "71343b9c", + "id": "b8a64f73", "metadata": {}, "source": [ "by adding a list of edges," @@ -163,7 +163,7 @@ { "cell_type": "code", "execution_count": null, - "id": "374bd8d0", + "id": "d8138ac4", "metadata": {}, "outputs": [], "source": [ @@ -172,7 +172,7 @@ }, { "cell_type": "markdown", - "id": "644f4993", + "id": "b078e838", "metadata": {}, "source": [ "or by adding any ebunch of edges. An *ebunch* is any iterable\n", @@ -185,7 +185,7 @@ { "cell_type": "code", "execution_count": null, - "id": "074baa2e", + "id": "fa225e53", "metadata": {}, "outputs": [], "source": [ @@ -194,7 +194,7 @@ }, { "cell_type": "markdown", - "id": "e50eb6ae", + "id": "7d1b3654", "metadata": {}, "source": [ "There are no complaints when adding existing nodes or edges. For example,\n", @@ -204,7 +204,7 @@ { "cell_type": "code", "execution_count": null, - "id": "442ead0d", + "id": "947e7828", "metadata": {}, "outputs": [], "source": [ @@ -213,7 +213,7 @@ }, { "cell_type": "markdown", - "id": "4024ce66", + "id": "34cc62da", "metadata": {}, "source": [ "we add new nodes/edges and NetworkX quietly ignores any that are\n", @@ -223,7 +223,7 @@ { "cell_type": "code", "execution_count": null, - "id": "1a28eba3", + "id": "1abaca76", "metadata": {}, "outputs": [], "source": [ @@ -237,7 +237,7 @@ }, { "cell_type": "markdown", - "id": "8f01b50f", + "id": "9c8b3551", "metadata": {}, "source": [ "At this stage the graph `G` consists of 8 nodes and 3 edges, as can be seen by:" @@ -246,7 +246,7 @@ { "cell_type": "code", "execution_count": null, - "id": "d64242ae", + "id": "8d945779", "metadata": {}, "outputs": [], "source": [ @@ -257,7 +257,7 @@ { "cell_type": "code", "execution_count": null, - "id": "769ece0a", + "id": "8106e641", "metadata": {}, "outputs": [], "source": [ @@ -272,7 +272,7 @@ }, { "cell_type": "markdown", - "id": "01f32e3f", + "id": "7776ebf0", "metadata": {}, "source": [ "# Examining elements of a graph\n", @@ -292,7 +292,7 @@ { "cell_type": "code", "execution_count": null, - "id": "5cac6d0c", + "id": "f1db6fe4", "metadata": {}, "outputs": [], "source": [ @@ -304,7 +304,7 @@ }, { "cell_type": "markdown", - "id": "b4eaac19", + "id": "326966d8", "metadata": {}, "source": [ "One can specify to report the edges and degree from a subset of all nodes\n", @@ -316,7 +316,7 @@ { "cell_type": "code", "execution_count": null, - "id": "9a569e8e", + "id": "a887bd22", "metadata": {}, "outputs": [], "source": [ @@ -326,7 +326,7 @@ }, { "cell_type": "markdown", - "id": "3792b737", + "id": "0e95153e", "metadata": {}, "source": [ "# Removing elements from a graph\n", @@ -343,7 +343,7 @@ { "cell_type": "code", "execution_count": null, - "id": "0c03b461", + "id": "93137ba0", "metadata": {}, "outputs": [], "source": [ @@ -355,7 +355,7 @@ }, { "cell_type": "markdown", - "id": "c32458b5", + "id": "6bece1a1", "metadata": {}, "source": [ "# Using the graph constructors\n", @@ -370,7 +370,7 @@ { "cell_type": "code", "execution_count": null, - "id": "5a1db134", + "id": "34430c44", "metadata": {}, "outputs": [], "source": [ @@ -387,7 +387,7 @@ }, { "cell_type": "markdown", - "id": "e923f4ea", + "id": "631ff23d", "metadata": {}, "source": [ "# What to use as nodes and edges\n", @@ -416,7 +416,7 @@ { "cell_type": "code", "execution_count": null, - "id": "5c36cc7b", + "id": "99a5b8f8", "metadata": {}, "outputs": [], "source": [ @@ -428,7 +428,7 @@ }, { "cell_type": "markdown", - "id": "fc7ba8ed", + "id": "d8c15355", "metadata": {}, "source": [ "You can get/set the attributes of an edge using subscript notation\n", @@ -438,7 +438,7 @@ { "cell_type": "code", "execution_count": null, - "id": "d7c96ea9", + "id": "f6c0f87c", "metadata": {}, "outputs": [], "source": [ @@ -450,7 +450,7 @@ }, { "cell_type": "markdown", - "id": "c52488ed", + "id": "3a5e2995", "metadata": {}, "source": [ "Fast examination of all (node, adjacency) pairs is achieved using\n", @@ -461,7 +461,7 @@ { "cell_type": "code", "execution_count": null, - "id": "4ad41762", + "id": "030a4a4d", "metadata": {}, "outputs": [], "source": [ @@ -475,7 +475,7 @@ }, { "cell_type": "markdown", - "id": "282b2908", + "id": "a8a9a94a", "metadata": {}, "source": [ "Convenient access to all edges is achieved with the edges property." @@ -484,7 +484,7 @@ { "cell_type": "code", "execution_count": null, - "id": "4e430e32", + "id": "55f2a3e8", "metadata": {}, "outputs": [], "source": [ @@ -495,7 +495,7 @@ }, { "cell_type": "markdown", - "id": "98bedbed", + "id": "967fe442", "metadata": {}, "source": [ "# Adding attributes to graphs, nodes, and edges\n", @@ -517,7 +517,7 @@ { "cell_type": "code", "execution_count": null, - "id": "4f84ab28", + "id": "35759e03", "metadata": {}, "outputs": [], "source": [ @@ -527,7 +527,7 @@ }, { "cell_type": "markdown", - "id": "94bb6842", + "id": "536cb1e0", "metadata": {}, "source": [ "Or you can modify attributes later" @@ -536,7 +536,7 @@ { "cell_type": "code", "execution_count": null, - "id": "2dd0bdcf", + "id": "cee37c27", "metadata": {}, "outputs": [], "source": [ @@ -546,7 +546,7 @@ }, { "cell_type": "markdown", - "id": "8fe0bcdb", + "id": "e5859bc2", "metadata": {}, "source": [ "# Node attributes\n", @@ -557,7 +557,7 @@ { "cell_type": "code", "execution_count": null, - "id": "069cb96e", + "id": "509abb93", "metadata": {}, "outputs": [], "source": [ @@ -570,7 +570,7 @@ }, { "cell_type": "markdown", - "id": "096455d1", + "id": "c87af800", "metadata": {}, "source": [ "Note that adding a node to `G.nodes` does not add it to the graph, use\n", @@ -585,7 +585,7 @@ { "cell_type": "code", "execution_count": null, - "id": "265def8f", + "id": "c669880f", "metadata": {}, "outputs": [], "source": [ @@ -598,7 +598,7 @@ }, { "cell_type": "markdown", - "id": "4cd7effb", + "id": "cdd119f1", "metadata": {}, "source": [ "The special attribute `weight` should be numeric as it is used by\n", @@ -619,7 +619,7 @@ { "cell_type": "code", "execution_count": null, - "id": "185f205c", + "id": "188a0581", "metadata": {}, "outputs": [], "source": [ @@ -633,7 +633,7 @@ }, { "cell_type": "markdown", - "id": "f9a02938", + "id": "67ef6a71", "metadata": {}, "source": [ "Some algorithms work only for directed graphs and others are not well\n", @@ -646,7 +646,7 @@ { "cell_type": "code", "execution_count": null, - "id": "45b7266c", + "id": "bef2a5dd", "metadata": {}, "outputs": [], "source": [ @@ -655,7 +655,7 @@ }, { "cell_type": "markdown", - "id": "6c1f86a5", + "id": "32f625b5", "metadata": {}, "source": [ "# Multigraphs\n", @@ -675,7 +675,7 @@ { "cell_type": "code", "execution_count": null, - "id": "53e8279a", + "id": "9afbc429", "metadata": {}, "outputs": [], "source": [ @@ -693,7 +693,7 @@ }, { "cell_type": "markdown", - "id": "b2fc0430", + "id": "5f3fb92c", "metadata": {}, "source": [ "# Graph generators and graph operations\n", @@ -713,7 +713,7 @@ { "cell_type": "code", "execution_count": null, - "id": "98becbe7", + "id": "dfbfbccd", "metadata": {}, "outputs": [], "source": [ @@ -725,7 +725,7 @@ }, { "cell_type": "markdown", - "id": "f64b207e", + "id": "ff571c7d", "metadata": {}, "source": [ "# 4. Using a stochastic graph generator, e.g,\n", @@ -736,7 +736,7 @@ { "cell_type": "code", "execution_count": null, - "id": "0e3fbcc1", + "id": "dbd5ef2a", "metadata": {}, "outputs": [], "source": [ @@ -748,7 +748,7 @@ }, { "cell_type": "markdown", - "id": "e2bc154f", + "id": "b8546121", "metadata": {}, "source": [ "# 5. Reading a graph stored in a file using common graph formats\n", @@ -760,7 +760,7 @@ { "cell_type": "code", "execution_count": null, - "id": "b8123219", + "id": "618f48a4", "metadata": {}, "outputs": [], "source": [ @@ -770,7 +770,7 @@ }, { "cell_type": "markdown", - "id": "a9d71fee", + "id": "cbf17408", "metadata": {}, "source": [ "For details on graph formats see Reading and writing graphs\n", @@ -785,7 +785,7 @@ { "cell_type": "code", "execution_count": null, - "id": "cc6c16f8", + "id": "bab63117", "metadata": {}, "outputs": [], "source": [ @@ -799,7 +799,7 @@ }, { "cell_type": "markdown", - "id": "9f383e7f", + "id": "76a41131", "metadata": {}, "source": [ "Some functions with large output iterate over (node, value) 2-tuples.\n", @@ -809,7 +809,7 @@ { "cell_type": "code", "execution_count": null, - "id": "f6974dc1", + "id": "07fbc523", "metadata": {}, "outputs": [], "source": [ @@ -819,7 +819,7 @@ }, { "cell_type": "markdown", - "id": "d5afc098", + "id": "3978385c", "metadata": {}, "source": [ "See Algorithms for details on graph algorithms\n", @@ -838,7 +838,7 @@ { "cell_type": "code", "execution_count": null, - "id": "b7ecc573", + "id": "661afff3", "metadata": {}, "outputs": [], "source": [ @@ -847,7 +847,7 @@ }, { "cell_type": "markdown", - "id": "de776715", + "id": "d3b9e423", "metadata": {}, "source": [ "To test if the import of `nx_pylab` was successful draw `G`\n", @@ -857,7 +857,7 @@ { "cell_type": "code", "execution_count": null, - "id": "349f33a7", + "id": "d33340f6", "metadata": {}, "outputs": [], "source": [ @@ -870,7 +870,7 @@ }, { "cell_type": "markdown", - "id": "ed0d421d", + "id": "449ba321", "metadata": {}, "source": [ "when drawing to an interactive display. Note that you may need to issue a\n", @@ -880,7 +880,7 @@ { "cell_type": "code", "execution_count": null, - "id": "122ca624", + "id": "60189d8d", "metadata": {}, "outputs": [], "source": [ @@ -889,7 +889,7 @@ }, { "cell_type": "markdown", - "id": "460fb049", + "id": "d967d169", "metadata": {}, "source": [ "command if you are not using matplotlib in interactive mode." @@ -898,7 +898,7 @@ { "cell_type": "code", "execution_count": null, - "id": "de1fe032", + "id": "f4cc346e", "metadata": {}, "outputs": [], "source": [ @@ -919,7 +919,7 @@ }, { "cell_type": "markdown", - "id": "99d31c49", + "id": "4c682dc4", "metadata": {}, "source": [ "You can find additional options via `draw_networkx()` and\n", @@ -930,7 +930,7 @@ { "cell_type": "code", "execution_count": null, - "id": "bbe8955c", + "id": "50b65c1a", "metadata": {}, "outputs": [], "source": [ @@ -941,7 +941,7 @@ }, { "cell_type": "markdown", - "id": "09190ce3", + "id": "657979bb", "metadata": {}, "source": [ "To save drawings to a file, use, for example" @@ -950,7 +950,7 @@ { "cell_type": "code", "execution_count": null, - "id": "6c9a8927", + "id": "0bf2381c", "metadata": {}, "outputs": [], "source": [ @@ -960,7 +960,7 @@ }, { "cell_type": "markdown", - "id": "8c631785", + "id": "0745862f", "metadata": {}, "source": [ "This function writes to the file `path.png` in the local directory. If Graphviz and\n", @@ -973,7 +973,7 @@ { "cell_type": "code", "execution_count": null, - "id": "ff77bfcb", + "id": "cd4aad41", "metadata": {}, "outputs": [], "source": [ @@ -985,7 +985,7 @@ }, { "cell_type": "markdown", - "id": "e5d21b97", + "id": "30bc0300", "metadata": {}, "source": [ "See Drawing for additional details." diff --git a/tutorial_full.ipynb b/tutorial_full.ipynb index 2a1a7431..1c97a5b6 100644 --- a/tutorial_full.ipynb +++ b/tutorial_full.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "e140aad2", + "id": "17631eeb", "metadata": {}, "source": [ "## Tutorial\n", @@ -17,13 +17,13 @@ { "cell_type": "code", "execution_count": 1, - "id": "a76a2b61", + "id": "8b43999d", "metadata": { "execution": { - "iopub.execute_input": "2022-12-14T17:19:50.793962Z", - "iopub.status.busy": "2022-12-14T17:19:50.793345Z", - "iopub.status.idle": "2022-12-14T17:19:50.867912Z", - "shell.execute_reply": "2022-12-14T17:19:50.867186Z" + "iopub.execute_input": "2022-12-15T16:09:58.892605Z", + "iopub.status.busy": "2022-12-15T16:09:58.892265Z", + "iopub.status.idle": "2022-12-15T16:09:58.962180Z", + "shell.execute_reply": "2022-12-15T16:09:58.961564Z" } }, "outputs": [], @@ -34,7 +34,7 @@ }, { "cell_type": "markdown", - "id": "16943313", + "id": "26ff4033", "metadata": {}, "source": [ "By definition, a `Graph` is a collection of nodes (vertices) along with\n", @@ -54,13 +54,13 @@ { "cell_type": "code", "execution_count": 2, - "id": "61914daf", + "id": "0fe5988f", "metadata": { "execution": { - "iopub.execute_input": "2022-12-14T17:19:50.871694Z", - "iopub.status.busy": "2022-12-14T17:19:50.871289Z", - "iopub.status.idle": "2022-12-14T17:19:50.874663Z", - "shell.execute_reply": "2022-12-14T17:19:50.874004Z" + "iopub.execute_input": "2022-12-15T16:09:58.965271Z", + "iopub.status.busy": "2022-12-15T16:09:58.965047Z", + "iopub.status.idle": "2022-12-15T16:09:58.967968Z", + "shell.execute_reply": "2022-12-15T16:09:58.967351Z" } }, "outputs": [], @@ -70,7 +70,7 @@ }, { "cell_type": "markdown", - "id": "6823de84", + "id": "222e2bae", "metadata": {}, "source": [ "or add nodes from any [iterable](https://docs.python.org/3/glossary.html#term-iterable) container, such as a list" @@ -79,13 +79,13 @@ { "cell_type": "code", "execution_count": 3, - "id": "d1ce14dd", + "id": "57fe5cf9", "metadata": { "execution": { - "iopub.execute_input": "2022-12-14T17:19:50.877627Z", - "iopub.status.busy": "2022-12-14T17:19:50.877405Z", - "iopub.status.idle": "2022-12-14T17:19:50.880689Z", - "shell.execute_reply": "2022-12-14T17:19:50.880064Z" + "iopub.execute_input": "2022-12-15T16:09:58.970772Z", + "iopub.status.busy": "2022-12-15T16:09:58.970569Z", + "iopub.status.idle": "2022-12-15T16:09:58.973424Z", + "shell.execute_reply": "2022-12-15T16:09:58.972817Z" } }, "outputs": [], @@ -95,7 +95,7 @@ }, { "cell_type": "markdown", - "id": "cf35f78c", + "id": "6df8a05b", "metadata": {}, "source": [ "You can also add nodes along with node\n", @@ -117,13 +117,13 @@ { "cell_type": "code", "execution_count": 4, - "id": "0568aa4e", + "id": "289a49be", "metadata": { "execution": { - "iopub.execute_input": "2022-12-14T17:19:50.883781Z", - "iopub.status.busy": "2022-12-14T17:19:50.883565Z", - "iopub.status.idle": "2022-12-14T17:19:50.887081Z", - "shell.execute_reply": "2022-12-14T17:19:50.886423Z" + "iopub.execute_input": "2022-12-15T16:09:58.976257Z", + "iopub.status.busy": "2022-12-15T16:09:58.976058Z", + "iopub.status.idle": "2022-12-15T16:09:58.979272Z", + "shell.execute_reply": "2022-12-15T16:09:58.978648Z" } }, "outputs": [], @@ -134,7 +134,7 @@ }, { "cell_type": "markdown", - "id": "80937cd6", + "id": "c0cfeabf", "metadata": {}, "source": [ "`G` now contains the nodes of `H` as nodes of `G`.\n", @@ -144,13 +144,13 @@ { "cell_type": "code", "execution_count": 5, - "id": "662f7aea", + "id": "15930f02", "metadata": { "execution": { - "iopub.execute_input": "2022-12-14T17:19:50.889981Z", - "iopub.status.busy": "2022-12-14T17:19:50.889771Z", - "iopub.status.idle": "2022-12-14T17:19:50.892655Z", - "shell.execute_reply": "2022-12-14T17:19:50.892025Z" + "iopub.execute_input": "2022-12-15T16:09:58.981908Z", + "iopub.status.busy": "2022-12-15T16:09:58.981696Z", + "iopub.status.idle": "2022-12-15T16:09:58.984470Z", + "shell.execute_reply": "2022-12-15T16:09:58.983874Z" } }, "outputs": [], @@ -160,7 +160,7 @@ }, { "cell_type": "markdown", - "id": "a07ca9e2", + "id": "4fe1f15d", "metadata": {}, "source": [ "The graph `G` now contains `H` as a node. This flexibility is very powerful as\n", @@ -178,13 +178,13 @@ { "cell_type": "code", "execution_count": 6, - "id": "731da224", + "id": "8ec67ec0", "metadata": { "execution": { - "iopub.execute_input": "2022-12-14T17:19:50.895668Z", - "iopub.status.busy": "2022-12-14T17:19:50.895457Z", - "iopub.status.idle": "2022-12-14T17:19:50.898590Z", - "shell.execute_reply": "2022-12-14T17:19:50.897934Z" + "iopub.execute_input": "2022-12-15T16:09:58.987099Z", + "iopub.status.busy": "2022-12-15T16:09:58.986885Z", + "iopub.status.idle": "2022-12-15T16:09:58.989895Z", + "shell.execute_reply": "2022-12-15T16:09:58.989298Z" } }, "outputs": [], @@ -196,7 +196,7 @@ }, { "cell_type": "markdown", - "id": "71343b9c", + "id": "b8a64f73", "metadata": {}, "source": [ "by adding a list of edges," @@ -205,13 +205,13 @@ { "cell_type": "code", "execution_count": 7, - "id": "374bd8d0", + "id": "d8138ac4", "metadata": { "execution": { - "iopub.execute_input": "2022-12-14T17:19:50.901573Z", - "iopub.status.busy": "2022-12-14T17:19:50.901360Z", - "iopub.status.idle": "2022-12-14T17:19:50.904395Z", - "shell.execute_reply": "2022-12-14T17:19:50.903773Z" + "iopub.execute_input": "2022-12-15T16:09:58.992728Z", + "iopub.status.busy": "2022-12-15T16:09:58.992526Z", + "iopub.status.idle": "2022-12-15T16:09:58.995489Z", + "shell.execute_reply": "2022-12-15T16:09:58.994833Z" } }, "outputs": [], @@ -221,7 +221,7 @@ }, { "cell_type": "markdown", - "id": "644f4993", + "id": "b078e838", "metadata": {}, "source": [ "or by adding any ebunch of edges. An *ebunch* is any iterable\n", @@ -234,13 +234,13 @@ { "cell_type": "code", "execution_count": 8, - "id": "074baa2e", + "id": "fa225e53", "metadata": { "execution": { - "iopub.execute_input": "2022-12-14T17:19:50.907429Z", - "iopub.status.busy": "2022-12-14T17:19:50.907209Z", - "iopub.status.idle": "2022-12-14T17:19:50.910153Z", - "shell.execute_reply": "2022-12-14T17:19:50.909527Z" + "iopub.execute_input": "2022-12-15T16:09:58.998184Z", + "iopub.status.busy": "2022-12-15T16:09:58.997979Z", + "iopub.status.idle": "2022-12-15T16:09:59.000786Z", + "shell.execute_reply": "2022-12-15T16:09:59.000186Z" } }, "outputs": [], @@ -250,7 +250,7 @@ }, { "cell_type": "markdown", - "id": "e50eb6ae", + "id": "7d1b3654", "metadata": {}, "source": [ "There are no complaints when adding existing nodes or edges. For example,\n", @@ -260,13 +260,13 @@ { "cell_type": "code", "execution_count": 9, - "id": "442ead0d", + "id": "947e7828", "metadata": { "execution": { - "iopub.execute_input": "2022-12-14T17:19:50.913115Z", - "iopub.status.busy": "2022-12-14T17:19:50.912906Z", - "iopub.status.idle": "2022-12-14T17:19:50.915756Z", - "shell.execute_reply": "2022-12-14T17:19:50.915106Z" + "iopub.execute_input": "2022-12-15T16:09:59.003559Z", + "iopub.status.busy": "2022-12-15T16:09:59.003352Z", + "iopub.status.idle": "2022-12-15T16:09:59.006076Z", + "shell.execute_reply": "2022-12-15T16:09:59.005471Z" } }, "outputs": [], @@ -276,7 +276,7 @@ }, { "cell_type": "markdown", - "id": "4024ce66", + "id": "34cc62da", "metadata": {}, "source": [ "we add new nodes/edges and NetworkX quietly ignores any that are\n", @@ -286,13 +286,13 @@ { "cell_type": "code", "execution_count": 10, - "id": "1a28eba3", + "id": "1abaca76", "metadata": { "execution": { - "iopub.execute_input": "2022-12-14T17:19:50.918858Z", - "iopub.status.busy": "2022-12-14T17:19:50.918631Z", - "iopub.status.idle": "2022-12-14T17:19:50.922663Z", - "shell.execute_reply": "2022-12-14T17:19:50.921977Z" + "iopub.execute_input": "2022-12-15T16:09:59.008868Z", + "iopub.status.busy": "2022-12-15T16:09:59.008660Z", + "iopub.status.idle": "2022-12-15T16:09:59.012320Z", + "shell.execute_reply": "2022-12-15T16:09:59.011712Z" } }, "outputs": [], @@ -307,7 +307,7 @@ }, { "cell_type": "markdown", - "id": "8f01b50f", + "id": "9c8b3551", "metadata": {}, "source": [ "At this stage the graph `G` consists of 8 nodes and 3 edges, as can be seen by:" @@ -316,13 +316,13 @@ { "cell_type": "code", "execution_count": 11, - "id": "d64242ae", + "id": "8d945779", "metadata": { "execution": { - "iopub.execute_input": "2022-12-14T17:19:50.926072Z", - "iopub.status.busy": "2022-12-14T17:19:50.925855Z", - "iopub.status.idle": "2022-12-14T17:19:50.932305Z", - "shell.execute_reply": "2022-12-14T17:19:50.931680Z" + "iopub.execute_input": "2022-12-15T16:09:59.014910Z", + "iopub.status.busy": "2022-12-15T16:09:59.014700Z", + "iopub.status.idle": "2022-12-15T16:09:59.020831Z", + "shell.execute_reply": "2022-12-15T16:09:59.020228Z" } }, "outputs": [ @@ -345,13 +345,13 @@ { "cell_type": "code", "execution_count": 12, - "id": "769ece0a", + "id": "8106e641", "metadata": { "execution": { - "iopub.execute_input": "2022-12-14T17:19:50.936615Z", - "iopub.status.busy": "2022-12-14T17:19:50.936398Z", - "iopub.status.idle": "2022-12-14T17:19:50.940810Z", - "shell.execute_reply": "2022-12-14T17:19:50.940166Z" + "iopub.execute_input": "2022-12-15T16:09:59.024547Z", + "iopub.status.busy": "2022-12-15T16:09:59.024344Z", + "iopub.status.idle": "2022-12-15T16:09:59.028517Z", + "shell.execute_reply": "2022-12-15T16:09:59.027910Z" } }, "outputs": [], @@ -367,7 +367,7 @@ }, { "cell_type": "markdown", - "id": "01f32e3f", + "id": "7776ebf0", "metadata": {}, "source": [ "# Examining elements of a graph\n", @@ -387,13 +387,13 @@ { "cell_type": "code", "execution_count": 13, - "id": "5cac6d0c", + "id": "f1db6fe4", "metadata": { "execution": { - "iopub.execute_input": "2022-12-14T17:19:50.943791Z", - "iopub.status.busy": "2022-12-14T17:19:50.943576Z", - "iopub.status.idle": "2022-12-14T17:19:50.948181Z", - "shell.execute_reply": "2022-12-14T17:19:50.947549Z" + "iopub.execute_input": "2022-12-15T16:09:59.031217Z", + "iopub.status.busy": "2022-12-15T16:09:59.030997Z", + "iopub.status.idle": "2022-12-15T16:09:59.035317Z", + "shell.execute_reply": "2022-12-15T16:09:59.034716Z" } }, "outputs": [ @@ -417,7 +417,7 @@ }, { "cell_type": "markdown", - "id": "b4eaac19", + "id": "326966d8", "metadata": {}, "source": [ "One can specify to report the edges and degree from a subset of all nodes\n", @@ -429,13 +429,13 @@ { "cell_type": "code", "execution_count": 14, - "id": "9a569e8e", + "id": "a887bd22", "metadata": { "execution": { - "iopub.execute_input": "2022-12-14T17:19:50.952535Z", - "iopub.status.busy": "2022-12-14T17:19:50.951914Z", - "iopub.status.idle": "2022-12-14T17:19:50.956679Z", - "shell.execute_reply": "2022-12-14T17:19:50.956036Z" + "iopub.execute_input": "2022-12-15T16:09:59.038895Z", + "iopub.status.busy": "2022-12-15T16:09:59.038690Z", + "iopub.status.idle": "2022-12-15T16:09:59.042738Z", + "shell.execute_reply": "2022-12-15T16:09:59.042151Z" } }, "outputs": [ @@ -457,7 +457,7 @@ }, { "cell_type": "markdown", - "id": "3792b737", + "id": "0e95153e", "metadata": {}, "source": [ "# Removing elements from a graph\n", @@ -474,13 +474,13 @@ { "cell_type": "code", "execution_count": 15, - "id": "0c03b461", + "id": "93137ba0", "metadata": { "execution": { - "iopub.execute_input": "2022-12-14T17:19:50.959907Z", - "iopub.status.busy": "2022-12-14T17:19:50.959473Z", - "iopub.status.idle": "2022-12-14T17:19:50.963032Z", - "shell.execute_reply": "2022-12-14T17:19:50.962335Z" + "iopub.execute_input": "2022-12-15T16:09:59.046004Z", + "iopub.status.busy": "2022-12-15T16:09:59.045797Z", + "iopub.status.idle": "2022-12-15T16:09:59.048909Z", + "shell.execute_reply": "2022-12-15T16:09:59.048305Z" } }, "outputs": [], @@ -493,7 +493,7 @@ }, { "cell_type": "markdown", - "id": "c32458b5", + "id": "6bece1a1", "metadata": {}, "source": [ "# Using the graph constructors\n", @@ -508,13 +508,13 @@ { "cell_type": "code", "execution_count": 16, - "id": "5a1db134", + "id": "34430c44", "metadata": { "execution": { - "iopub.execute_input": "2022-12-14T17:19:50.966080Z", - "iopub.status.busy": "2022-12-14T17:19:50.965714Z", - "iopub.status.idle": "2022-12-14T17:19:51.239853Z", - "shell.execute_reply": "2022-12-14T17:19:51.239169Z" + "iopub.execute_input": "2022-12-15T16:09:59.051519Z", + "iopub.status.busy": "2022-12-15T16:09:59.051317Z", + "iopub.status.idle": "2022-12-15T16:09:59.299859Z", + "shell.execute_reply": "2022-12-15T16:09:59.299260Z" } }, "outputs": [ @@ -543,7 +543,7 @@ }, { "cell_type": "markdown", - "id": "e923f4ea", + "id": "631ff23d", "metadata": {}, "source": [ "# What to use as nodes and edges\n", @@ -572,13 +572,13 @@ { "cell_type": "code", "execution_count": 17, - "id": "5c36cc7b", + "id": "99a5b8f8", "metadata": { "execution": { - "iopub.execute_input": "2022-12-14T17:19:51.243339Z", - "iopub.status.busy": "2022-12-14T17:19:51.242972Z", - "iopub.status.idle": "2022-12-14T17:19:51.248533Z", - "shell.execute_reply": "2022-12-14T17:19:51.248022Z" + "iopub.execute_input": "2022-12-15T16:09:59.303483Z", + "iopub.status.busy": "2022-12-15T16:09:59.303001Z", + "iopub.status.idle": "2022-12-15T16:09:59.310194Z", + "shell.execute_reply": "2022-12-15T16:09:59.309623Z" } }, "outputs": [ @@ -602,7 +602,7 @@ }, { "cell_type": "markdown", - "id": "fc7ba8ed", + "id": "d8c15355", "metadata": {}, "source": [ "You can get/set the attributes of an edge using subscript notation\n", @@ -612,13 +612,13 @@ { "cell_type": "code", "execution_count": 18, - "id": "d7c96ea9", + "id": "f6c0f87c", "metadata": { "execution": { - "iopub.execute_input": "2022-12-14T17:19:51.251538Z", - "iopub.status.busy": "2022-12-14T17:19:51.251096Z", - "iopub.status.idle": "2022-12-14T17:19:51.255800Z", - "shell.execute_reply": "2022-12-14T17:19:51.255304Z" + "iopub.execute_input": "2022-12-15T16:09:59.313133Z", + "iopub.status.busy": "2022-12-15T16:09:59.312602Z", + "iopub.status.idle": "2022-12-15T16:09:59.319403Z", + "shell.execute_reply": "2022-12-15T16:09:59.318849Z" } }, "outputs": [ @@ -642,7 +642,7 @@ }, { "cell_type": "markdown", - "id": "c52488ed", + "id": "3a5e2995", "metadata": {}, "source": [ "Fast examination of all (node, adjacency) pairs is achieved using\n", @@ -653,13 +653,13 @@ { "cell_type": "code", "execution_count": 19, - "id": "4ad41762", + "id": "030a4a4d", "metadata": { "execution": { - "iopub.execute_input": "2022-12-14T17:19:51.258696Z", - "iopub.status.busy": "2022-12-14T17:19:51.258325Z", - "iopub.status.idle": "2022-12-14T17:19:51.264140Z", - "shell.execute_reply": "2022-12-14T17:19:51.263371Z" + "iopub.execute_input": "2022-12-15T16:09:59.323339Z", + "iopub.status.busy": "2022-12-15T16:09:59.322123Z", + "iopub.status.idle": "2022-12-15T16:09:59.328488Z", + "shell.execute_reply": "2022-12-15T16:09:59.327934Z" } }, "outputs": [ @@ -685,7 +685,7 @@ }, { "cell_type": "markdown", - "id": "282b2908", + "id": "a8a9a94a", "metadata": {}, "source": [ "Convenient access to all edges is achieved with the edges property." @@ -694,13 +694,13 @@ { "cell_type": "code", "execution_count": 20, - "id": "4e430e32", + "id": "55f2a3e8", "metadata": { "execution": { - "iopub.execute_input": "2022-12-14T17:19:51.267683Z", - "iopub.status.busy": "2022-12-14T17:19:51.267100Z", - "iopub.status.idle": "2022-12-14T17:19:51.272669Z", - "shell.execute_reply": "2022-12-14T17:19:51.271922Z" + "iopub.execute_input": "2022-12-15T16:09:59.332429Z", + "iopub.status.busy": "2022-12-15T16:09:59.331244Z", + "iopub.status.idle": "2022-12-15T16:09:59.336688Z", + "shell.execute_reply": "2022-12-15T16:09:59.336101Z" } }, "outputs": [ @@ -721,7 +721,7 @@ }, { "cell_type": "markdown", - "id": "98bedbed", + "id": "967fe442", "metadata": {}, "source": [ "# Adding attributes to graphs, nodes, and edges\n", @@ -743,13 +743,13 @@ { "cell_type": "code", "execution_count": 21, - "id": "4f84ab28", + "id": "35759e03", "metadata": { "execution": { - "iopub.execute_input": "2022-12-14T17:19:51.275964Z", - "iopub.status.busy": "2022-12-14T17:19:51.275522Z", - "iopub.status.idle": "2022-12-14T17:19:51.282015Z", - "shell.execute_reply": "2022-12-14T17:19:51.281427Z" + "iopub.execute_input": "2022-12-15T16:09:59.339815Z", + "iopub.status.busy": "2022-12-15T16:09:59.339366Z", + "iopub.status.idle": "2022-12-15T16:09:59.345359Z", + "shell.execute_reply": "2022-12-15T16:09:59.344799Z" } }, "outputs": [ @@ -771,7 +771,7 @@ }, { "cell_type": "markdown", - "id": "94bb6842", + "id": "536cb1e0", "metadata": {}, "source": [ "Or you can modify attributes later" @@ -780,13 +780,13 @@ { "cell_type": "code", "execution_count": 22, - "id": "2dd0bdcf", + "id": "cee37c27", "metadata": { "execution": { - "iopub.execute_input": "2022-12-14T17:19:51.285319Z", - "iopub.status.busy": "2022-12-14T17:19:51.284925Z", - "iopub.status.idle": "2022-12-14T17:19:51.290438Z", - "shell.execute_reply": "2022-12-14T17:19:51.289799Z" + "iopub.execute_input": "2022-12-15T16:09:59.347997Z", + "iopub.status.busy": "2022-12-15T16:09:59.347787Z", + "iopub.status.idle": "2022-12-15T16:09:59.353597Z", + "shell.execute_reply": "2022-12-15T16:09:59.353057Z" } }, "outputs": [ @@ -808,7 +808,7 @@ }, { "cell_type": "markdown", - "id": "8fe0bcdb", + "id": "e5859bc2", "metadata": {}, "source": [ "# Node attributes\n", @@ -819,13 +819,13 @@ { "cell_type": "code", "execution_count": 23, - "id": "069cb96e", + "id": "509abb93", "metadata": { "execution": { - "iopub.execute_input": "2022-12-14T17:19:51.293540Z", - "iopub.status.busy": "2022-12-14T17:19:51.293178Z", - "iopub.status.idle": "2022-12-14T17:19:51.299717Z", - "shell.execute_reply": "2022-12-14T17:19:51.299074Z" + "iopub.execute_input": "2022-12-15T16:09:59.356191Z", + "iopub.status.busy": "2022-12-15T16:09:59.355985Z", + "iopub.status.idle": "2022-12-15T16:09:59.362567Z", + "shell.execute_reply": "2022-12-15T16:09:59.362016Z" } }, "outputs": [ @@ -850,7 +850,7 @@ }, { "cell_type": "markdown", - "id": "096455d1", + "id": "c87af800", "metadata": {}, "source": [ "Note that adding a node to `G.nodes` does not add it to the graph, use\n", @@ -865,13 +865,13 @@ { "cell_type": "code", "execution_count": 24, - "id": "265def8f", + "id": "c669880f", "metadata": { "execution": { - "iopub.execute_input": "2022-12-14T17:19:51.302729Z", - "iopub.status.busy": "2022-12-14T17:19:51.302354Z", - "iopub.status.idle": "2022-12-14T17:19:51.306783Z", - "shell.execute_reply": "2022-12-14T17:19:51.306102Z" + "iopub.execute_input": "2022-12-15T16:09:59.365306Z", + "iopub.status.busy": "2022-12-15T16:09:59.365110Z", + "iopub.status.idle": "2022-12-15T16:09:59.369068Z", + "shell.execute_reply": "2022-12-15T16:09:59.368445Z" } }, "outputs": [], @@ -885,7 +885,7 @@ }, { "cell_type": "markdown", - "id": "4cd7effb", + "id": "cdd119f1", "metadata": {}, "source": [ "The special attribute `weight` should be numeric as it is used by\n", @@ -906,13 +906,13 @@ { "cell_type": "code", "execution_count": 25, - "id": "185f205c", + "id": "188a0581", "metadata": { "execution": { - "iopub.execute_input": "2022-12-14T17:19:51.310120Z", - "iopub.status.busy": "2022-12-14T17:19:51.309650Z", - "iopub.status.idle": "2022-12-14T17:19:51.315584Z", - "shell.execute_reply": "2022-12-14T17:19:51.314912Z" + "iopub.execute_input": "2022-12-15T16:09:59.371856Z", + "iopub.status.busy": "2022-12-15T16:09:59.371657Z", + "iopub.status.idle": "2022-12-15T16:09:59.376683Z", + "shell.execute_reply": "2022-12-15T16:09:59.376082Z" } }, "outputs": [ @@ -938,7 +938,7 @@ }, { "cell_type": "markdown", - "id": "f9a02938", + "id": "67ef6a71", "metadata": {}, "source": [ "Some algorithms work only for directed graphs and others are not well\n", @@ -951,13 +951,13 @@ { "cell_type": "code", "execution_count": 26, - "id": "45b7266c", + "id": "bef2a5dd", "metadata": { "execution": { - "iopub.execute_input": "2022-12-14T17:19:51.320081Z", - "iopub.status.busy": "2022-12-14T17:19:51.319565Z", - "iopub.status.idle": "2022-12-14T17:19:51.323180Z", - "shell.execute_reply": "2022-12-14T17:19:51.322438Z" + "iopub.execute_input": "2022-12-15T16:09:59.380045Z", + "iopub.status.busy": "2022-12-15T16:09:59.379848Z", + "iopub.status.idle": "2022-12-15T16:09:59.382711Z", + "shell.execute_reply": "2022-12-15T16:09:59.382115Z" } }, "outputs": [], @@ -967,7 +967,7 @@ }, { "cell_type": "markdown", - "id": "6c1f86a5", + "id": "32f625b5", "metadata": {}, "source": [ "# Multigraphs\n", @@ -987,13 +987,13 @@ { "cell_type": "code", "execution_count": 27, - "id": "53e8279a", + "id": "9afbc429", "metadata": { "execution": { - "iopub.execute_input": "2022-12-14T17:19:51.326422Z", - "iopub.status.busy": "2022-12-14T17:19:51.325995Z", - "iopub.status.idle": "2022-12-14T17:19:51.333227Z", - "shell.execute_reply": "2022-12-14T17:19:51.332558Z" + "iopub.execute_input": "2022-12-15T16:09:59.385450Z", + "iopub.status.busy": "2022-12-15T16:09:59.385250Z", + "iopub.status.idle": "2022-12-15T16:09:59.391730Z", + "shell.execute_reply": "2022-12-15T16:09:59.391153Z" } }, "outputs": [ @@ -1023,7 +1023,7 @@ }, { "cell_type": "markdown", - "id": "b2fc0430", + "id": "5f3fb92c", "metadata": {}, "source": [ "# Graph generators and graph operations\n", @@ -1043,13 +1043,13 @@ { "cell_type": "code", "execution_count": 28, - "id": "98becbe7", + "id": "dfbfbccd", "metadata": { "execution": { - "iopub.execute_input": "2022-12-14T17:19:51.337290Z", - "iopub.status.busy": "2022-12-14T17:19:51.336910Z", - "iopub.status.idle": "2022-12-14T17:19:51.341566Z", - "shell.execute_reply": "2022-12-14T17:19:51.340899Z" + "iopub.execute_input": "2022-12-15T16:09:59.394310Z", + "iopub.status.busy": "2022-12-15T16:09:59.394108Z", + "iopub.status.idle": "2022-12-15T16:09:59.398324Z", + "shell.execute_reply": "2022-12-15T16:09:59.397712Z" } }, "outputs": [], @@ -1062,7 +1062,7 @@ }, { "cell_type": "markdown", - "id": "f64b207e", + "id": "ff571c7d", "metadata": {}, "source": [ "# 4. Using a stochastic graph generator, e.g,\n", @@ -1073,13 +1073,13 @@ { "cell_type": "code", "execution_count": 29, - "id": "0e3fbcc1", + "id": "dbd5ef2a", "metadata": { "execution": { - "iopub.execute_input": "2022-12-14T17:19:51.344694Z", - "iopub.status.busy": "2022-12-14T17:19:51.344472Z", - "iopub.status.idle": "2022-12-14T17:19:51.362430Z", - "shell.execute_reply": "2022-12-14T17:19:51.361627Z" + "iopub.execute_input": "2022-12-15T16:09:59.400914Z", + "iopub.status.busy": "2022-12-15T16:09:59.400704Z", + "iopub.status.idle": "2022-12-15T16:09:59.415729Z", + "shell.execute_reply": "2022-12-15T16:09:59.415171Z" } }, "outputs": [], @@ -1092,7 +1092,7 @@ }, { "cell_type": "markdown", - "id": "e2bc154f", + "id": "b8546121", "metadata": {}, "source": [ "# 5. Reading a graph stored in a file using common graph formats\n", @@ -1104,13 +1104,13 @@ { "cell_type": "code", "execution_count": 30, - "id": "b8123219", + "id": "618f48a4", "metadata": { "execution": { - "iopub.execute_input": "2022-12-14T17:19:51.365756Z", - "iopub.status.busy": "2022-12-14T17:19:51.365488Z", - "iopub.status.idle": "2022-12-14T17:19:51.921608Z", - "shell.execute_reply": "2022-12-14T17:19:51.920879Z" + "iopub.execute_input": "2022-12-15T16:09:59.418492Z", + "iopub.status.busy": "2022-12-15T16:09:59.418290Z", + "iopub.status.idle": "2022-12-15T16:09:59.850088Z", + "shell.execute_reply": "2022-12-15T16:09:59.849443Z" } }, "outputs": [], @@ -1121,7 +1121,7 @@ }, { "cell_type": "markdown", - "id": "a9d71fee", + "id": "cbf17408", "metadata": {}, "source": [ "For details on graph formats see Reading and writing graphs\n", @@ -1136,13 +1136,13 @@ { "cell_type": "code", "execution_count": 31, - "id": "cc6c16f8", + "id": "bab63117", "metadata": { "execution": { - "iopub.execute_input": "2022-12-14T17:19:51.925538Z", - "iopub.status.busy": "2022-12-14T17:19:51.925098Z", - "iopub.status.idle": "2022-12-14T17:19:51.934220Z", - "shell.execute_reply": "2022-12-14T17:19:51.933622Z" + "iopub.execute_input": "2022-12-15T16:09:59.853313Z", + "iopub.status.busy": "2022-12-15T16:09:59.853057Z", + "iopub.status.idle": "2022-12-15T16:09:59.861263Z", + "shell.execute_reply": "2022-12-15T16:09:59.860728Z" } }, "outputs": [ @@ -1168,7 +1168,7 @@ }, { "cell_type": "markdown", - "id": "9f383e7f", + "id": "76a41131", "metadata": {}, "source": [ "Some functions with large output iterate over (node, value) 2-tuples.\n", @@ -1178,13 +1178,13 @@ { "cell_type": "code", "execution_count": 32, - "id": "f6974dc1", + "id": "07fbc523", "metadata": { "execution": { - "iopub.execute_input": "2022-12-14T17:19:51.937173Z", - "iopub.status.busy": "2022-12-14T17:19:51.936933Z", - "iopub.status.idle": "2022-12-14T17:19:51.941740Z", - "shell.execute_reply": "2022-12-14T17:19:51.941081Z" + "iopub.execute_input": "2022-12-15T16:09:59.864076Z", + "iopub.status.busy": "2022-12-15T16:09:59.863870Z", + "iopub.status.idle": "2022-12-15T16:09:59.868065Z", + "shell.execute_reply": "2022-12-15T16:09:59.867461Z" } }, "outputs": [ @@ -1206,7 +1206,7 @@ }, { "cell_type": "markdown", - "id": "d5afc098", + "id": "3978385c", "metadata": {}, "source": [ "See Algorithms for details on graph algorithms\n", @@ -1225,13 +1225,13 @@ { "cell_type": "code", "execution_count": 33, - "id": "b7ecc573", + "id": "661afff3", "metadata": { "execution": { - "iopub.execute_input": "2022-12-14T17:19:51.945537Z", - "iopub.status.busy": "2022-12-14T17:19:51.945163Z", - "iopub.status.idle": "2022-12-14T17:19:52.331909Z", - "shell.execute_reply": "2022-12-14T17:19:52.331163Z" + "iopub.execute_input": "2022-12-15T16:09:59.871575Z", + "iopub.status.busy": "2022-12-15T16:09:59.871373Z", + "iopub.status.idle": "2022-12-15T16:10:00.172789Z", + "shell.execute_reply": "2022-12-15T16:10:00.172148Z" } }, "outputs": [], @@ -1241,7 +1241,7 @@ }, { "cell_type": "markdown", - "id": "de776715", + "id": "d3b9e423", "metadata": {}, "source": [ "To test if the import of `nx_pylab` was successful draw `G`\n", @@ -1251,19 +1251,19 @@ { "cell_type": "code", "execution_count": 34, - "id": "349f33a7", + "id": "d33340f6", "metadata": { "execution": { - "iopub.execute_input": "2022-12-14T17:19:52.336052Z", - "iopub.status.busy": "2022-12-14T17:19:52.335455Z", - "iopub.status.idle": "2022-12-14T17:19:52.550715Z", - "shell.execute_reply": "2022-12-14T17:19:52.549777Z" + "iopub.execute_input": "2022-12-15T16:10:00.176199Z", + "iopub.status.busy": "2022-12-15T16:10:00.175872Z", + "iopub.status.idle": "2022-12-15T16:10:00.412388Z", + "shell.execute_reply": "2022-12-15T16:10:00.411845Z" } }, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "<Figure size 640x480 with 2 Axes>" ] @@ -1282,7 +1282,7 @@ }, { "cell_type": "markdown", - "id": "ed0d421d", + "id": "449ba321", "metadata": {}, "source": [ "when drawing to an interactive display. Note that you may need to issue a\n", @@ -1292,13 +1292,13 @@ { "cell_type": "code", "execution_count": 35, - "id": "122ca624", + "id": "60189d8d", "metadata": { "execution": { - "iopub.execute_input": "2022-12-14T17:19:52.554597Z", - "iopub.status.busy": "2022-12-14T17:19:52.553952Z", - "iopub.status.idle": "2022-12-14T17:19:52.557338Z", - "shell.execute_reply": "2022-12-14T17:19:52.556814Z" + "iopub.execute_input": "2022-12-15T16:10:00.415429Z", + "iopub.status.busy": "2022-12-15T16:10:00.415080Z", + "iopub.status.idle": "2022-12-15T16:10:00.417827Z", + "shell.execute_reply": "2022-12-15T16:10:00.417352Z" } }, "outputs": [], @@ -1308,7 +1308,7 @@ }, { "cell_type": "markdown", - "id": "460fb049", + "id": "d967d169", "metadata": {}, "source": [ "command if you are not using matplotlib in interactive mode." @@ -1317,19 +1317,19 @@ { "cell_type": "code", "execution_count": 36, - "id": "de1fe032", + "id": "f4cc346e", "metadata": { "execution": { - "iopub.execute_input": "2022-12-14T17:19:52.560358Z", - "iopub.status.busy": "2022-12-14T17:19:52.559889Z", - "iopub.status.idle": "2022-12-14T17:19:52.851297Z", - "shell.execute_reply": "2022-12-14T17:19:52.850676Z" + "iopub.execute_input": "2022-12-15T16:10:00.420457Z", + "iopub.status.busy": "2022-12-15T16:10:00.420147Z", + "iopub.status.idle": "2022-12-15T16:10:00.682477Z", + "shell.execute_reply": "2022-12-15T16:10:00.681840Z" } }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "<Figure size 640x480 with 4 Axes>" ] @@ -1356,7 +1356,7 @@ }, { "cell_type": "markdown", - "id": "99d31c49", + "id": "4c682dc4", "metadata": {}, "source": [ "You can find additional options via `draw_networkx()` and\n", @@ -1367,13 +1367,13 @@ { "cell_type": "code", "execution_count": 37, - "id": "bbe8955c", + "id": "50b65c1a", "metadata": { "execution": { - "iopub.execute_input": "2022-12-14T17:19:52.855718Z", - "iopub.status.busy": "2022-12-14T17:19:52.855135Z", - "iopub.status.idle": "2022-12-14T17:19:52.966298Z", - "shell.execute_reply": "2022-12-14T17:19:52.965626Z" + "iopub.execute_input": "2022-12-15T16:10:00.685937Z", + "iopub.status.busy": "2022-12-15T16:10:00.685600Z", + "iopub.status.idle": "2022-12-15T16:10:00.789968Z", + "shell.execute_reply": "2022-12-15T16:10:00.789322Z" } }, "outputs": [ @@ -1396,7 +1396,7 @@ }, { "cell_type": "markdown", - "id": "09190ce3", + "id": "657979bb", "metadata": {}, "source": [ "To save drawings to a file, use, for example" @@ -1405,19 +1405,19 @@ { "cell_type": "code", "execution_count": 38, - "id": "6c9a8927", + "id": "0bf2381c", "metadata": { "execution": { - "iopub.execute_input": "2022-12-14T17:19:52.969943Z", - "iopub.status.busy": "2022-12-14T17:19:52.969244Z", - "iopub.status.idle": "2022-12-14T17:19:53.105576Z", - "shell.execute_reply": "2022-12-14T17:19:53.104990Z" + "iopub.execute_input": "2022-12-15T16:10:00.793315Z", + "iopub.status.busy": "2022-12-15T16:10:00.792900Z", + "iopub.status.idle": "2022-12-15T16:10:00.923470Z", + "shell.execute_reply": "2022-12-15T16:10:00.922798Z" } }, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -1433,7 +1433,7 @@ }, { "cell_type": "markdown", - "id": "8c631785", + "id": "0745862f", "metadata": {}, "source": [ "This function writes to the file `path.png` in the local directory. If Graphviz and\n", @@ -1446,13 +1446,13 @@ { "cell_type": "code", "execution_count": 39, - "id": "ff77bfcb", + "id": "cd4aad41", "metadata": { "execution": { - "iopub.execute_input": "2022-12-14T17:19:53.108736Z", - "iopub.status.busy": "2022-12-14T17:19:53.108328Z", - "iopub.status.idle": "2022-12-14T17:19:53.247604Z", - "shell.execute_reply": "2022-12-14T17:19:53.246889Z" + "iopub.execute_input": "2022-12-15T16:10:00.927106Z", + "iopub.status.busy": "2022-12-15T16:10:00.926739Z", + "iopub.status.idle": "2022-12-15T16:10:01.060822Z", + "shell.execute_reply": "2022-12-15T16:10:01.060165Z" } }, "outputs": [ @@ -1476,7 +1476,7 @@ }, { "cell_type": "markdown", - "id": "e5d21b97", + "id": "30bc0300", "metadata": {}, "source": [ "See Drawing for additional details." @@ -1494,7 +1494,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, |
