Jun 26, 2023 · Jun 26, 2023 · Jul 3, 2023
diff --git a/graphblas/io/_networkx.py b/graphblas/io/_networkx.py


 # 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
    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
    -------
        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
    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"])
Original file line number	Diff line number	Diff line change
Expand Up		@@ -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
Expand All		@@ -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
		-------
Expand All		@@ -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
Original file line number	Diff line number	Diff line change
Expand Up		@@ -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"])
Expand Down