diff --git a/graphblas/io/_networkx.py b/graphblas/io/_networkx.py
index 2324a11c2..3c62f27f2 100644
--- a/graphblas/io/_networkx.py
+++ b/graphblas/io/_networkx.py
@@ -31,7 +31,7 @@ def from_networkx(G, nodelist=None, dtype=None, weight="weight", name=None):
 
 
 # TODO: add parameters to allow different networkx classes and attribute names
-def to_networkx(m, edge_attribute="weight"):
+def to_networkx(m, edge_attribute="weight", create_isolates=False):
     """Create a networkx DiGraph from a square adjacency Matrix.
 
     Parameters
@@ -41,6 +41,11 @@ def to_networkx(m, edge_attribute="weight"):
     edge_attribute : str, optional
         Name of edge attribute from values of Matrix. If None, values will be skipped.
         Default is "weight".
+    create_isolates : bool, optional
+        If row $i$ and column $i$ are both empty, then node $i$ is an isolate. By default,
+        these isolate nodes are ignored. If True, then the graph will also contain the
+        isolate nodes. Note that for very large adjacency matrices, creating isolates
+        will increase the memory footprint of the graph.
 
     Returns
     -------
@@ -56,4 +61,6 @@ def to_networkx(m, edge_attribute="weight"):
         G.add_edges_from(zip(rows, cols))
     else:
         G.add_weighted_edges_from(zip(rows, cols, vals.tolist()), weight=edge_attribute)
+    if create_isolates:
+        G.add_nodes_from(range(max(m.shape)))
     return G
diff --git a/graphblas/tests/test_io.py b/graphblas/tests/test_io.py
index bf2ca2015..417ce0b50 100644
--- a/graphblas/tests/test_io.py
+++ b/graphblas/tests/test_io.py
@@ -169,6 +169,17 @@ def test_matrix_to_from_networkx():
     assert M.nvals == 0
     assert M.shape == (1, 1)
 
+    # test creation of isolates
+    M = gb.Matrix.from_edgelist([(0,1),(3,0)],nrows=5,ncols=5)
+    G = gb.io.to_networkx(M, create_isolates=True)
+    G_nx = nx.DiGraph()
+    G_nx.add_edges_from([(0,1),(3,0)])
+    G_nx.add_nodes_from(range(5))
+    assert G.number_of_edges() == G_nx.number_of_edges() == 2
+    assert G.number_of_nodes() == G_nx.number_of_nodes() == 5
+    from networkx.utils import graphs_equal
+    assert graphs_equal(G, G_nx)
+
 
 @pytest.mark.skipif("not ss")
 @pytest.mark.parametrize("engine", ["auto", "scipy", "fmm"])