PR for issue #6033 Improve test coverage for algorithms in betweenness_subset.py #6033 (#6083)

* Updated test_betweenness_centrality_subset.py

* add test of normalized in test_betweenness_centrality_subset.py

* add test of normalized in test_betweenness_centrality_subset.py

* update test of normalized in test_betweenness_centrality_subset.py

* update weight of test_betweenness_centrality_subset.py

* add docstring

* add docstring in test_betweenness_centrality_subset.py

* add docstring in test_betweenness_centrality_subset.py

1 files changed, 131 insertions, 0 deletions

diff --git a/networkx/algorithms/centrality/tests/test_betweenness_centrality_subset.py b/networkx/algorithms/centrality/tests/test_betweenness_centrality_subset.py
index 44c98287..a35a401a 100644
--- a/networkx/algorithms/centrality/tests/test_betweenness_centrality_subset.py
+++ b/networkx/algorithms/centrality/tests/test_betweenness_centrality_subset.py
@@ -116,6 +116,51 @@ class TestSubsetBetweennessCentrality:
         for n in sorted(G):
             assert b[n] == pytest.approx(expected_b[n], abs=1e-7)
 
+    def test_normalized_p2(self):
+        """
+        Betweenness Centrality Subset: Normalized P2
+        if n <= 2:  no normalization, betweenness centrality should be 0 for all nodes.
+        """
+        G = nx.Graph()
+        nx.add_path(G, range(2))
+        b_answer = {0: 0, 1: 0.0}
+        b = nx.betweenness_centrality_subset(
+            G, sources=[0], targets=[1], normalized=True, weight=None
+        )
+        for n in sorted(G):
+            assert b[n] == pytest.approx(b_answer[n], abs=1e-7)
+
+    def test_normalized_P5_directed(self):
+        """Betweenness Centrality Subset: Normalized Directed P5"""
+        G = nx.DiGraph()
+        nx.add_path(G, range(5))
+        b_answer = {0: 0, 1: 1.0 / 12.0, 2: 1.0 / 12.0, 3: 0, 4: 0, 5: 0}
+        b = nx.betweenness_centrality_subset(
+            G, sources=[0], targets=[3], normalized=True, weight=None
+        )
+        for n in sorted(G):
+            assert b[n] == pytest.approx(b_answer[n], abs=1e-7)
+
+    def test_weighted_graph(self):
+        """Betweenness Centrality Subset: Weighted Graph"""
+        G = nx.DiGraph()
+        G.add_edge(0, 1, weight=3)
+        G.add_edge(0, 2, weight=2)
+        G.add_edge(0, 3, weight=6)
+        G.add_edge(0, 4, weight=4)
+        G.add_edge(1, 3, weight=5)
+        G.add_edge(1, 5, weight=5)
+        G.add_edge(2, 4, weight=1)
+        G.add_edge(3, 4, weight=2)
+        G.add_edge(3, 5, weight=1)
+        G.add_edge(4, 5, weight=4)
+        b_answer = {0: 0.0, 1: 0.0, 2: 0.5, 3: 0.5, 4: 0.5, 5: 0.0}
+        b = nx.betweenness_centrality_subset(
+            G, sources=[0], targets=[5], normalized=False, weight="weight"
+        )
+        for n in sorted(G):
+            assert b[n] == pytest.approx(b_answer[n], abs=1e-7)
+
 
 class TestEdgeSubsetBetweennessCentrality:
     def test_K5(self):
@@ -207,3 +252,89 @@ class TestEdgeSubsetBetweennessCentrality:
         )
         for n in sorted(G.edges()):
             assert b[n] == pytest.approx(b_answer[n], abs=1e-7)
+
+    def test_diamond_multi_path(self):
+        """Edge betweenness subset centrality: Diamond Multi Path"""
+        G = nx.Graph()
+        G.add_edges_from(
+            [
+                (1, 2),
+                (1, 3),
+                (1, 4),
+                (1, 5),
+                (1, 10),
+                (10, 11),
+                (11, 12),
+                (12, 9),
+                (2, 6),
+                (3, 6),
+                (4, 6),
+                (5, 7),
+                (7, 8),
+                (6, 8),
+                (8, 9),
+            ]
+        )
+        b_answer = dict.fromkeys(G.edges(), 0)
+        b_answer[(8, 9)] = 0.4
+        b_answer[(6, 8)] = b_answer[(7, 8)] = 0.2
+        b_answer[(2, 6)] = b_answer[(3, 6)] = b_answer[(4, 6)] = 0.2 / 3.0
+        b_answer[(1, 2)] = b_answer[(1, 3)] = b_answer[(1, 4)] = 0.2 / 3.0
+        b_answer[(5, 7)] = 0.2
+        b_answer[(1, 5)] = 0.2
+        b_answer[(9, 12)] = 0.1
+        b_answer[(11, 12)] = b_answer[(10, 11)] = b_answer[(1, 10)] = 0.1
+        b = nx.edge_betweenness_centrality_subset(
+            G, sources=[1], targets=[9], weight=None
+        )
+        for n in G.edges():
+            sort_n = tuple(sorted(n))
+            assert b[n] == pytest.approx(b_answer[sort_n], abs=1e-7)
+
+    def test_normalized_p1(self):
+        """
+        Edge betweenness subset centrality: P1
+        if n <= 1: no normalization b=0 for all nodes
+        """
+        G = nx.Graph()
+        nx.add_path(G, range(1))
+        b_answer = dict.fromkeys(G.edges(), 0)
+        b = nx.edge_betweenness_centrality_subset(
+            G, sources=[0], targets=[0], normalized=True, weight=None
+        )
+        for n in G.edges():
+            assert b[n] == pytest.approx(b_answer[n], abs=1e-7)
+
+    def test_normalized_P5_directed(self):
+        """Edge betweenness subset centrality: Normalized Directed P5"""
+        G = nx.DiGraph()
+        nx.add_path(G, range(5))
+        b_answer = dict.fromkeys(G.edges(), 0)
+        b_answer[(0, 1)] = b_answer[(1, 2)] = b_answer[(2, 3)] = 0.05
+        b = nx.edge_betweenness_centrality_subset(
+            G, sources=[0], targets=[3], normalized=True, weight=None
+        )
+        for n in G.edges():
+            assert b[n] == pytest.approx(b_answer[n], abs=1e-7)
+
+    def test_weighted_graph(self):
+        """Edge betweenness subset centrality: Weighted Graph"""
+        G = nx.DiGraph()
+        G.add_edge(0, 1, weight=3)
+        G.add_edge(0, 2, weight=2)
+        G.add_edge(0, 3, weight=6)
+        G.add_edge(0, 4, weight=4)
+        G.add_edge(1, 3, weight=5)
+        G.add_edge(1, 5, weight=5)
+        G.add_edge(2, 4, weight=1)
+        G.add_edge(3, 4, weight=2)
+        G.add_edge(3, 5, weight=1)
+        G.add_edge(4, 5, weight=4)
+        b_answer = dict.fromkeys(G.edges(), 0)
+        b_answer[(0, 2)] = b_answer[(2, 4)] = b_answer[(4, 5)] = 0.5
+        b_answer[(0, 3)] = b_answer[(3, 5)] = 0.5
+        b = nx.edge_betweenness_centrality_subset(
+            G, sources=[0], targets=[5], normalized=False, weight="weight"
+        )
+        for n in G.edges():
+            assert b[n] == pytest.approx(b_answer[n], abs=1e-7)