Fix a few broken links in the html docs (#4572)

* Fix 3 broken refs in nxep2.

* Fix see also link to average_neighbor_degree.

* rm ref to neighbor_connectivity.

* Fix links to bipartite sets and is_bipartite from centrality.

* Fix links to square_clustering.

5 files changed, 19 insertions, 20 deletions

diff --git a/doc/developer/nxeps/nxep-0002.rst b/doc/developer/nxeps/nxep-0002.rst
index 2fd77b81..a9e8e744 100644
--- a/doc/developer/nxeps/nxep-0002.rst
+++ b/doc/developer/nxeps/nxep-0002.rst
@@ -16,10 +16,10 @@ Abstract
 Iterating over a subset of nodes or edges in a graph is a very common 
 operation in networkx analysis.
 The graph classes in NetworkX (e.g. :class:`~networkx.Graph`,
-:class:`~newtorkx.DiGraph`, :class:`~networkx.MultiGraph`, etc.) expose the
-node and edge data of the graph via :meth:`nodes` and :meth:`edges`, which 
-return dict view objects, `NodeView` (or `NodeDataView`) and `EdgeView` 
-(or `EdgeDataView`), respectively.
+:class:`~networkx.DiGraph`, :class:`~networkx.MultiGraph`, etc.) expose the
+node and edge data of the graph via :meth:`~networkx.Graph.nodes` and
+:meth:`~networkx.Graph.edges`, which return dict view objects, `NodeView`
+(or `NodeDataView`) and `EdgeView` (or `EdgeDataView`), respectively.
 The node and edge `View` classes have dict-like semantics for item access,
 returning the data dict corresponding to a given node or edge.
 This NXEP proposes adding support for slicing to the relevant node & edge
diff --git a/networkx/algorithms/assortativity/connectivity.py b/networkx/algorithms/assortativity/connectivity.py
index 391477cd..a00a2314 100644
--- a/networkx/algorithms/assortativity/connectivity.py
+++ b/networkx/algorithms/assortativity/connectivity.py
@@ -59,9 +59,9 @@ def average_degree_connectivity(
     >>> nx.k_nearest_neighbors(G, weight="weight")
     {1: 2.0, 2: 1.75}
 
-    See also
+    See Also
     --------
-    neighbors_average_degree
+    average_neighbor_degree
 
     Notes
     -----
diff --git a/networkx/algorithms/assortativity/correlation.py b/networkx/algorithms/assortativity/correlation.py
index 7136f2bd..bcf2ef3c 100644
--- a/networkx/algorithms/assortativity/correlation.py
+++ b/networkx/algorithms/assortativity/correlation.py
@@ -56,7 +56,6 @@ def degree_assortativity_coefficient(G, x="out", y="in", weight=None, nodes=None
     --------
     attribute_assortativity_coefficient
     numeric_assortativity_coefficient
-    neighbor_connectivity
     degree_mixing_dict
     degree_mixing_matrix
 
diff --git a/networkx/algorithms/bipartite/centrality.py b/networkx/algorithms/bipartite/centrality.py
index ef76a1f4..09415c5f 100644
--- a/networkx/algorithms/bipartite/centrality.py
+++ b/networkx/algorithms/bipartite/centrality.py
@@ -24,10 +24,10 @@ def degree_centrality(G, nodes):
 
     See Also
     --------
-    betweenness_centrality,
-    closeness_centrality,
-    sets,
-    is_bipartite
+    betweenness_centrality
+    closeness_centrality
+    :func:`~networkx.algorithms.bipartite.basic.sets`
+    :func:`~networkx.algorithms.bipartite.basic.is_bipartite`
 
     Notes
     -----
@@ -122,10 +122,10 @@ def betweenness_centrality(G, nodes):
 
     See Also
     --------
-    degree_centrality,
-    closeness_centrality,
-    sets,
-    is_bipartite
+    degree_centrality
+    closeness_centrality
+    :func:`~networkx.algorithms.bipartite.basic.sets`
+    :func:`~networkx.algorithms.bipartite.basic.is_bipartite`
 
     Notes
     -----
@@ -194,10 +194,10 @@ def closeness_centrality(G, nodes, normalized=True):
 
     See Also
     --------
-    betweenness_centrality,
+    betweenness_centrality
     degree_centrality
-    sets,
-    is_bipartite
+    :func:`~networkx.algorithms.bipartite.basic.sets`
+    :func:`~networkx.algorithms.bipartite.basic.is_bipartite`
 
     Notes
     -----
diff --git a/networkx/algorithms/bipartite/cluster.py b/networkx/algorithms/bipartite/cluster.py
index 64343d7c..8d1f0547 100644
--- a/networkx/algorithms/bipartite/cluster.py
+++ b/networkx/algorithms/bipartite/cluster.py
@@ -96,8 +96,8 @@ def latapy_clustering(G, nodes=None, mode="dot"):
     See Also
     --------
     robins_alexander_clustering
-    square_clustering
     average_clustering
+    networkx.algorithms.cluster.square_clustering
 
     References
     ----------
@@ -239,7 +239,7 @@ def robins_alexander_clustering(G):
     See Also
     --------
     latapy_clustering
-    square_clustering
+    networkx.algorithms.cluster.square_clustering
 
     References
     ----------