diff --git a/networkx/linalg/graphmatrix.py b/networkx/linalg/graphmatrix.py
index 0b6c5426210..9f477bcccc5 100644
--- a/networkx/linalg/graphmatrix.py
+++ b/networkx/linalg/graphmatrix.py
@@ -107,7 +107,7 @@ def incidence_matrix(
 
 @nx._dispatchable(edge_attrs="weight")
 def adjacency_matrix(G, nodelist=None, dtype=None, weight="weight"):
-    """Returns adjacency matrix of G.
+    """Returns adjacency matrix of `G`.
 
     Parameters
     ----------
@@ -115,12 +115,13 @@ def adjacency_matrix(G, nodelist=None, dtype=None, weight="weight"):
        A NetworkX graph
 
     nodelist : list, optional
-       The rows and columns are ordered according to the nodes in nodelist.
-       If nodelist is None, then the ordering is produced by G.nodes().
+       The rows and columns are ordered according to the nodes in `nodelist`.
+       If ``nodelist=None`` (the default), then the ordering is produced by
+       ``G.nodes()``.
 
     dtype : NumPy data-type, optional
         The desired data-type for the array.
-        If None, then the NumPy default is used.
+        If `None`, then the NumPy default is used.
 
     weight : string or None, optional (default='weight')
        The edge data key used to provide each value in the matrix.
@@ -133,29 +134,29 @@ def adjacency_matrix(G, nodelist=None, dtype=None, weight="weight"):
 
     Notes
     -----
-    For directed graphs, entry i,j corresponds to an edge from i to j.
+    For directed graphs, entry ``i, j`` corresponds to an edge from ``i`` to ``j``.
 
     If you want a pure Python adjacency matrix representation try
-    networkx.convert.to_dict_of_dicts which will return a
+    :func:`~networkx.convert.to_dict_of_dicts` which will return a
     dictionary-of-dictionaries format that can be addressed as a
     sparse matrix.
 
-    For MultiGraph/MultiDiGraph with parallel edges the weights are summed.
-    See `to_numpy_array` for other options.
+    For multigraphs with parallel edges the weights are summed.
+    See :func:`networkx.convert_matrix.to_numpy_array` for other options.
 
     The convention used for self-loop edges in graphs is to assign the
     diagonal matrix entry value to the edge weight attribute
     (or the number 1 if the edge has no weight attribute).  If the
     alternate convention of doubling the edge weight is desired the
-    resulting SciPy sparse array can be modified as follows:
-
-    >>> G = nx.Graph([(1, 1)])
-    >>> A = nx.adjacency_matrix(G)
-    >>> print(A.todense())
-    [[1]]
-    >>> A.setdiag(A.diagonal() * 2)
-    >>> print(A.todense())
-    [[2]]
+    resulting SciPy sparse array can be modified as follows::
+
+        >>> G = nx.Graph([(1, 1)])
+        >>> A = nx.adjacency_matrix(G)
+        >>> A.toarray()
+        array([[1]])
+        >>> A.setdiag(A.diagonal() * 2)
+        >>> A.toarray()
+        array([[2]])
 
     See Also
     --------