two version of R2R are here HEAD master

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diff --git a/.venv/lib/python3.12/site-packages/networkx/algorithms/tests/test_similarity.py b/.venv/lib/python3.12/site-packages/networkx/algorithms/tests/test_similarity.py
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+import pytest
+
+import networkx as nx
+from networkx.algorithms.similarity import (
+    graph_edit_distance,
+    optimal_edit_paths,
+    optimize_graph_edit_distance,
+)
+from networkx.generators.classic import (
+    circular_ladder_graph,
+    cycle_graph,
+    path_graph,
+    wheel_graph,
+)
+
+
+def nmatch(n1, n2):
+    return n1 == n2
+
+
+def ematch(e1, e2):
+    return e1 == e2
+
+
+def getCanonical():
+    G = nx.Graph()
+    G.add_node("A", label="A")
+    G.add_node("B", label="B")
+    G.add_node("C", label="C")
+    G.add_node("D", label="D")
+    G.add_edge("A", "B", label="a-b")
+    G.add_edge("B", "C", label="b-c")
+    G.add_edge("B", "D", label="b-d")
+    return G
+
+
+class TestSimilarity:
+    @classmethod
+    def setup_class(cls):
+        global np
+        np = pytest.importorskip("numpy")
+        pytest.importorskip("scipy")
+
+    def test_graph_edit_distance_roots_and_timeout(self):
+        G0 = nx.star_graph(5)
+        G1 = G0.copy()
+        pytest.raises(ValueError, graph_edit_distance, G0, G1, roots=[2])
+        pytest.raises(ValueError, graph_edit_distance, G0, G1, roots=[2, 3, 4])
+        pytest.raises(nx.NodeNotFound, graph_edit_distance, G0, G1, roots=(9, 3))
+        pytest.raises(nx.NodeNotFound, graph_edit_distance, G0, G1, roots=(3, 9))
+        pytest.raises(nx.NodeNotFound, graph_edit_distance, G0, G1, roots=(9, 9))
+        assert graph_edit_distance(G0, G1, roots=(1, 2)) == 0
+        assert graph_edit_distance(G0, G1, roots=(0, 1)) == 8
+        assert graph_edit_distance(G0, G1, roots=(1, 2), timeout=5) == 0
+        assert graph_edit_distance(G0, G1, roots=(0, 1), timeout=5) == 8
+        assert graph_edit_distance(G0, G1, roots=(0, 1), timeout=0.0001) is None
+        # test raise on 0 timeout
+        pytest.raises(nx.NetworkXError, graph_edit_distance, G0, G1, timeout=0)
+
+    def test_graph_edit_distance(self):
+        G0 = nx.Graph()
+        G1 = path_graph(6)
+        G2 = cycle_graph(6)
+        G3 = wheel_graph(7)
+
+        assert graph_edit_distance(G0, G0) == 0
+        assert graph_edit_distance(G0, G1) == 11
+        assert graph_edit_distance(G1, G0) == 11
+        assert graph_edit_distance(G0, G2) == 12
+        assert graph_edit_distance(G2, G0) == 12
+        assert graph_edit_distance(G0, G3) == 19
+        assert graph_edit_distance(G3, G0) == 19
+
+        assert graph_edit_distance(G1, G1) == 0
+        assert graph_edit_distance(G1, G2) == 1
+        assert graph_edit_distance(G2, G1) == 1
+        assert graph_edit_distance(G1, G3) == 8
+        assert graph_edit_distance(G3, G1) == 8
+
+        assert graph_edit_distance(G2, G2) == 0
+        assert graph_edit_distance(G2, G3) == 7
+        assert graph_edit_distance(G3, G2) == 7
+
+        assert graph_edit_distance(G3, G3) == 0
+
+    def test_graph_edit_distance_node_match(self):
+        G1 = cycle_graph(5)
+        G2 = cycle_graph(5)
+        for n, attr in G1.nodes.items():
+            attr["color"] = "red" if n % 2 == 0 else "blue"
+        for n, attr in G2.nodes.items():
+            attr["color"] = "red" if n % 2 == 1 else "blue"
+        assert graph_edit_distance(G1, G2) == 0
+        assert (
+            graph_edit_distance(
+                G1, G2, node_match=lambda n1, n2: n1["color"] == n2["color"]
+            )
+            == 1
+        )
+
+    def test_graph_edit_distance_edge_match(self):
+        G1 = path_graph(6)
+        G2 = path_graph(6)
+        for e, attr in G1.edges.items():
+            attr["color"] = "red" if min(e) % 2 == 0 else "blue"
+        for e, attr in G2.edges.items():
+            attr["color"] = "red" if min(e) // 3 == 0 else "blue"
+        assert graph_edit_distance(G1, G2) == 0
+        assert (
+            graph_edit_distance(
+                G1, G2, edge_match=lambda e1, e2: e1["color"] == e2["color"]
+            )
+            == 2
+        )
+
+    def test_graph_edit_distance_node_cost(self):
+        G1 = path_graph(6)
+        G2 = path_graph(6)
+        for n, attr in G1.nodes.items():
+            attr["color"] = "red" if n % 2 == 0 else "blue"
+        for n, attr in G2.nodes.items():
+            attr["color"] = "red" if n % 2 == 1 else "blue"
+
+        def node_subst_cost(uattr, vattr):
+            if uattr["color"] == vattr["color"]:
+                return 1
+            else:
+                return 10
+
+        def node_del_cost(attr):
+            if attr["color"] == "blue":
+                return 20
+            else:
+                return 50
+
+        def node_ins_cost(attr):
+            if attr["color"] == "blue":
+                return 40
+            else:
+                return 100
+
+        assert (
+            graph_edit_distance(
+                G1,
+                G2,
+                node_subst_cost=node_subst_cost,
+                node_del_cost=node_del_cost,
+                node_ins_cost=node_ins_cost,
+            )
+            == 6
+        )
+
+    def test_graph_edit_distance_edge_cost(self):
+        G1 = path_graph(6)
+        G2 = path_graph(6)
+        for e, attr in G1.edges.items():
+            attr["color"] = "red" if min(e) % 2 == 0 else "blue"
+        for e, attr in G2.edges.items():
+            attr["color"] = "red" if min(e) // 3 == 0 else "blue"
+
+        def edge_subst_cost(gattr, hattr):
+            if gattr["color"] == hattr["color"]:
+                return 0.01
+            else:
+                return 0.1
+
+        def edge_del_cost(attr):
+            if attr["color"] == "blue":
+                return 0.2
+            else:
+                return 0.5
+
+        def edge_ins_cost(attr):
+            if attr["color"] == "blue":
+                return 0.4
+            else:
+                return 1.0
+
+        assert (
+            graph_edit_distance(
+                G1,
+                G2,
+                edge_subst_cost=edge_subst_cost,
+                edge_del_cost=edge_del_cost,
+                edge_ins_cost=edge_ins_cost,
+            )
+            == 0.23
+        )
+
+    def test_graph_edit_distance_upper_bound(self):
+        G1 = circular_ladder_graph(2)
+        G2 = circular_ladder_graph(6)
+        assert graph_edit_distance(G1, G2, upper_bound=5) is None
+        assert graph_edit_distance(G1, G2, upper_bound=24) == 22
+        assert graph_edit_distance(G1, G2) == 22
+
+    def test_optimal_edit_paths(self):
+        G1 = path_graph(3)
+        G2 = cycle_graph(3)
+        paths, cost = optimal_edit_paths(G1, G2)
+        assert cost == 1
+        assert len(paths) == 6
+
+        def canonical(vertex_path, edge_path):
+            return (
+                tuple(sorted(vertex_path)),
+                tuple(sorted(edge_path, key=lambda x: (None in x, x))),
+            )
+
+        expected_paths = [
+            (
+                [(0, 0), (1, 1), (2, 2)],
+                [((0, 1), (0, 1)), ((1, 2), (1, 2)), (None, (0, 2))],
+            ),
+            (
+                [(0, 0), (1, 2), (2, 1)],
+                [((0, 1), (0, 2)), ((1, 2), (1, 2)), (None, (0, 1))],
+            ),
+            (
+                [(0, 1), (1, 0), (2, 2)],
+                [((0, 1), (0, 1)), ((1, 2), (0, 2)), (None, (1, 2))],
+            ),
+            (
+                [(0, 1), (1, 2), (2, 0)],
+                [((0, 1), (1, 2)), ((1, 2), (0, 2)), (None, (0, 1))],
+            ),
+            (
+                [(0, 2), (1, 0), (2, 1)],
+                [((0, 1), (0, 2)), ((1, 2), (0, 1)), (None, (1, 2))],
+            ),
+            (
+                [(0, 2), (1, 1), (2, 0)],
+                [((0, 1), (1, 2)), ((1, 2), (0, 1)), (None, (0, 2))],
+            ),
+        ]
+        assert {canonical(*p) for p in paths} == {canonical(*p) for p in expected_paths}
+
+    def test_optimize_graph_edit_distance(self):
+        G1 = circular_ladder_graph(2)
+        G2 = circular_ladder_graph(6)
+        bestcost = 1000
+        for cost in optimize_graph_edit_distance(G1, G2):
+            assert cost < bestcost
+            bestcost = cost
+        assert bestcost == 22
+
+    # def test_graph_edit_distance_bigger(self):
+    #     G1 = circular_ladder_graph(12)
+    #     G2 = circular_ladder_graph(16)
+    #     assert_equal(graph_edit_distance(G1, G2), 22)
+
+    def test_selfloops(self):
+        G0 = nx.Graph()
+        G1 = nx.Graph()
+        G1.add_edges_from((("A", "A"), ("A", "B")))
+        G2 = nx.Graph()
+        G2.add_edges_from((("A", "B"), ("B", "B")))
+        G3 = nx.Graph()
+        G3.add_edges_from((("A", "A"), ("A", "B"), ("B", "B")))
+
+        assert graph_edit_distance(G0, G0) == 0
+        assert graph_edit_distance(G0, G1) == 4
+        assert graph_edit_distance(G1, G0) == 4
+        assert graph_edit_distance(G0, G2) == 4
+        assert graph_edit_distance(G2, G0) == 4
+        assert graph_edit_distance(G0, G3) == 5
+        assert graph_edit_distance(G3, G0) == 5
+
+        assert graph_edit_distance(G1, G1) == 0
+        assert graph_edit_distance(G1, G2) == 0
+        assert graph_edit_distance(G2, G1) == 0
+        assert graph_edit_distance(G1, G3) == 1
+        assert graph_edit_distance(G3, G1) == 1
+
+        assert graph_edit_distance(G2, G2) == 0
+        assert graph_edit_distance(G2, G3) == 1
+        assert graph_edit_distance(G3, G2) == 1
+
+        assert graph_edit_distance(G3, G3) == 0
+
+    def test_digraph(self):
+        G0 = nx.DiGraph()
+        G1 = nx.DiGraph()
+        G1.add_edges_from((("A", "B"), ("B", "C"), ("C", "D"), ("D", "A")))
+        G2 = nx.DiGraph()
+        G2.add_edges_from((("A", "B"), ("B", "C"), ("C", "D"), ("A", "D")))
+        G3 = nx.DiGraph()
+        G3.add_edges_from((("A", "B"), ("A", "C"), ("B", "D"), ("C", "D")))
+
+        assert graph_edit_distance(G0, G0) == 0
+        assert graph_edit_distance(G0, G1) == 8
+        assert graph_edit_distance(G1, G0) == 8
+        assert graph_edit_distance(G0, G2) == 8
+        assert graph_edit_distance(G2, G0) == 8
+        assert graph_edit_distance(G0, G3) == 8
+        assert graph_edit_distance(G3, G0) == 8
+
+        assert graph_edit_distance(G1, G1) == 0
+        assert graph_edit_distance(G1, G2) == 2
+        assert graph_edit_distance(G2, G1) == 2
+        assert graph_edit_distance(G1, G3) == 4
+        assert graph_edit_distance(G3, G1) == 4
+
+        assert graph_edit_distance(G2, G2) == 0
+        assert graph_edit_distance(G2, G3) == 2
+        assert graph_edit_distance(G3, G2) == 2
+
+        assert graph_edit_distance(G3, G3) == 0
+
+    def test_multigraph(self):
+        G0 = nx.MultiGraph()
+        G1 = nx.MultiGraph()
+        G1.add_edges_from((("A", "B"), ("B", "C"), ("A", "C")))
+        G2 = nx.MultiGraph()
+        G2.add_edges_from((("A", "B"), ("B", "C"), ("B", "C"), ("A", "C")))
+        G3 = nx.MultiGraph()
+        G3.add_edges_from((("A", "B"), ("B", "C"), ("A", "C"), ("A", "C"), ("A", "C")))
+
+        assert graph_edit_distance(G0, G0) == 0
+        assert graph_edit_distance(G0, G1) == 6
+        assert graph_edit_distance(G1, G0) == 6
+        assert graph_edit_distance(G0, G2) == 7
+        assert graph_edit_distance(G2, G0) == 7
+        assert graph_edit_distance(G0, G3) == 8
+        assert graph_edit_distance(G3, G0) == 8
+
+        assert graph_edit_distance(G1, G1) == 0
+        assert graph_edit_distance(G1, G2) == 1
+        assert graph_edit_distance(G2, G1) == 1
+        assert graph_edit_distance(G1, G3) == 2
+        assert graph_edit_distance(G3, G1) == 2
+
+        assert graph_edit_distance(G2, G2) == 0
+        assert graph_edit_distance(G2, G3) == 1
+        assert graph_edit_distance(G3, G2) == 1
+
+        assert graph_edit_distance(G3, G3) == 0
+
+    def test_multidigraph(self):
+        G1 = nx.MultiDiGraph()
+        G1.add_edges_from(
+            (
+                ("hardware", "kernel"),
+                ("kernel", "hardware"),
+                ("kernel", "userspace"),
+                ("userspace", "kernel"),
+            )
+        )
+        G2 = nx.MultiDiGraph()
+        G2.add_edges_from(
+            (
+                ("winter", "spring"),
+                ("spring", "summer"),
+                ("summer", "autumn"),
+                ("autumn", "winter"),
+            )
+        )
+
+        assert graph_edit_distance(G1, G2) == 5
+        assert graph_edit_distance(G2, G1) == 5
+
+    # by https://github.com/jfbeaumont
+    def testCopy(self):
+        G = nx.Graph()
+        G.add_node("A", label="A")
+        G.add_node("B", label="B")
+        G.add_edge("A", "B", label="a-b")
+        assert (
+            graph_edit_distance(G, G.copy(), node_match=nmatch, edge_match=ematch) == 0
+        )
+
+    def testSame(self):
+        G1 = nx.Graph()
+        G1.add_node("A", label="A")
+        G1.add_node("B", label="B")
+        G1.add_edge("A", "B", label="a-b")
+        G2 = nx.Graph()
+        G2.add_node("A", label="A")
+        G2.add_node("B", label="B")
+        G2.add_edge("A", "B", label="a-b")
+        assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 0
+
+    def testOneEdgeLabelDiff(self):
+        G1 = nx.Graph()
+        G1.add_node("A", label="A")
+        G1.add_node("B", label="B")
+        G1.add_edge("A", "B", label="a-b")
+        G2 = nx.Graph()
+        G2.add_node("A", label="A")
+        G2.add_node("B", label="B")
+        G2.add_edge("A", "B", label="bad")
+        assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 1
+
+    def testOneNodeLabelDiff(self):
+        G1 = nx.Graph()
+        G1.add_node("A", label="A")
+        G1.add_node("B", label="B")
+        G1.add_edge("A", "B", label="a-b")
+        G2 = nx.Graph()
+        G2.add_node("A", label="Z")
+        G2.add_node("B", label="B")
+        G2.add_edge("A", "B", label="a-b")
+        assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 1
+
+    def testOneExtraNode(self):
+        G1 = nx.Graph()
+        G1.add_node("A", label="A")
+        G1.add_node("B", label="B")
+        G1.add_edge("A", "B", label="a-b")
+        G2 = nx.Graph()
+        G2.add_node("A", label="A")
+        G2.add_node("B", label="B")
+        G2.add_edge("A", "B", label="a-b")
+        G2.add_node("C", label="C")
+        assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 1
+
+    def testOneExtraEdge(self):
+        G1 = nx.Graph()
+        G1.add_node("A", label="A")
+        G1.add_node("B", label="B")
+        G1.add_node("C", label="C")
+        G1.add_node("C", label="C")
+        G1.add_edge("A", "B", label="a-b")
+        G2 = nx.Graph()
+        G2.add_node("A", label="A")
+        G2.add_node("B", label="B")
+        G2.add_node("C", label="C")
+        G2.add_edge("A", "B", label="a-b")
+        G2.add_edge("A", "C", label="a-c")
+        assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 1
+
+    def testOneExtraNodeAndEdge(self):
+        G1 = nx.Graph()
+        G1.add_node("A", label="A")
+        G1.add_node("B", label="B")
+        G1.add_edge("A", "B", label="a-b")
+        G2 = nx.Graph()
+        G2.add_node("A", label="A")
+        G2.add_node("B", label="B")
+        G2.add_node("C", label="C")
+        G2.add_edge("A", "B", label="a-b")
+        G2.add_edge("A", "C", label="a-c")
+        assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 2
+
+    def testGraph1(self):
+        G1 = getCanonical()
+        G2 = nx.Graph()
+        G2.add_node("A", label="A")
+        G2.add_node("B", label="B")
+        G2.add_node("D", label="D")
+        G2.add_node("E", label="E")
+        G2.add_edge("A", "B", label="a-b")
+        G2.add_edge("B", "D", label="b-d")
+        G2.add_edge("D", "E", label="d-e")
+        assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 3
+
+    def testGraph2(self):
+        G1 = getCanonical()
+        G2 = nx.Graph()
+        G2.add_node("A", label="A")
+        G2.add_node("B", label="B")
+        G2.add_node("C", label="C")
+        G2.add_node("D", label="D")
+        G2.add_node("E", label="E")
+        G2.add_edge("A", "B", label="a-b")
+        G2.add_edge("B", "C", label="b-c")
+        G2.add_edge("C", "D", label="c-d")
+        G2.add_edge("C", "E", label="c-e")
+        assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 4
+
+    def testGraph3(self):
+        G1 = getCanonical()
+        G2 = nx.Graph()
+        G2.add_node("A", label="A")
+        G2.add_node("B", label="B")
+        G2.add_node("C", label="C")
+        G2.add_node("D", label="D")
+        G2.add_node("E", label="E")
+        G2.add_node("F", label="F")
+        G2.add_node("G", label="G")
+        G2.add_edge("A", "C", label="a-c")
+        G2.add_edge("A", "D", label="a-d")
+        G2.add_edge("D", "E", label="d-e")
+        G2.add_edge("D", "F", label="d-f")
+        G2.add_edge("D", "G", label="d-g")
+        G2.add_edge("E", "B", label="e-b")
+        assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 12
+
+    def testGraph4(self):
+        G1 = getCanonical()
+        G2 = nx.Graph()
+        G2.add_node("A", label="A")
+        G2.add_node("B", label="B")
+        G2.add_node("C", label="C")
+        G2.add_node("D", label="D")
+        G2.add_edge("A", "B", label="a-b")
+        G2.add_edge("B", "C", label="b-c")
+        G2.add_edge("C", "D", label="c-d")
+        assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 2
+
+    def testGraph4_a(self):
+        G1 = getCanonical()
+        G2 = nx.Graph()
+        G2.add_node("A", label="A")
+        G2.add_node("B", label="B")
+        G2.add_node("C", label="C")
+        G2.add_node("D", label="D")
+        G2.add_edge("A", "B", label="a-b")
+        G2.add_edge("B", "C", label="b-c")
+        G2.add_edge("A", "D", label="a-d")
+        assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 2
+
+    def testGraph4_b(self):
+        G1 = getCanonical()
+        G2 = nx.Graph()
+        G2.add_node("A", label="A")
+        G2.add_node("B", label="B")
+        G2.add_node("C", label="C")
+        G2.add_node("D", label="D")
+        G2.add_edge("A", "B", label="a-b")
+        G2.add_edge("B", "C", label="b-c")
+        G2.add_edge("B", "D", label="bad")
+        assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 1
+
+    # note: nx.simrank_similarity_numpy not included because returns np.array
+    simrank_algs = [
+        nx.simrank_similarity,
+        nx.algorithms.similarity._simrank_similarity_python,
+    ]
+
+    @pytest.mark.parametrize("simrank_similarity", simrank_algs)
+    def test_simrank_no_source_no_target(self, simrank_similarity):
+        G = nx.cycle_graph(5)
+        expected = {
+            0: {
+                0: 1,
+                1: 0.3951219505902448,
+                2: 0.5707317069281646,
+                3: 0.5707317069281646,
+                4: 0.3951219505902449,
+            },
+            1: {
+                0: 0.3951219505902448,
+                1: 1,
+                2: 0.3951219505902449,
+                3: 0.5707317069281646,
+                4: 0.5707317069281646,
+            },
+            2: {
+                0: 0.5707317069281646,
+                1: 0.3951219505902449,
+                2: 1,
+                3: 0.3951219505902449,
+                4: 0.5707317069281646,
+            },
+            3: {
+                0: 0.5707317069281646,
+                1: 0.5707317069281646,
+                2: 0.3951219505902449,
+                3: 1,
+                4: 0.3951219505902449,
+            },
+            4: {
+                0: 0.3951219505902449,
+                1: 0.5707317069281646,
+                2: 0.5707317069281646,
+                3: 0.3951219505902449,
+                4: 1,
+            },
+        }
+        actual = simrank_similarity(G)
+        for k, v in expected.items():
+            assert v == pytest.approx(actual[k], abs=1e-2)
+
+        # For a DiGraph test, use the first graph from the paper cited in
+        # the docs: https://dl.acm.org/doi/pdf/10.1145/775047.775126
+        G = nx.DiGraph()
+        G.add_node(0, label="Univ")
+        G.add_node(1, label="ProfA")
+        G.add_node(2, label="ProfB")
+        G.add_node(3, label="StudentA")
+        G.add_node(4, label="StudentB")
+        G.add_edges_from([(0, 1), (0, 2), (1, 3), (2, 4), (4, 2), (3, 0)])
+
+        expected = {
+            0: {0: 1, 1: 0.0, 2: 0.1323363991265798, 3: 0.0, 4: 0.03387811817640443},
+            1: {0: 0.0, 1: 1, 2: 0.4135512472705618, 3: 0.0, 4: 0.10586911930126384},
+            2: {
+                0: 0.1323363991265798,
+                1: 0.4135512472705618,
+                2: 1,
+                3: 0.04234764772050554,
+                4: 0.08822426608438655,
+            },
+            3: {0: 0.0, 1: 0.0, 2: 0.04234764772050554, 3: 1, 4: 0.3308409978164495},
+            4: {
+                0: 0.03387811817640443,
+                1: 0.10586911930126384,
+                2: 0.08822426608438655,
+                3: 0.3308409978164495,
+                4: 1,
+            },
+        }
+        # Use the importance_factor from the paper to get the same numbers.
+        actual = simrank_similarity(G, importance_factor=0.8)
+        for k, v in expected.items():
+            assert v == pytest.approx(actual[k], abs=1e-2)
+
+    @pytest.mark.parametrize("simrank_similarity", simrank_algs)
+    def test_simrank_source_no_target(self, simrank_similarity):
+        G = nx.cycle_graph(5)
+        expected = {
+            0: 1,
+            1: 0.3951219505902448,
+            2: 0.5707317069281646,
+            3: 0.5707317069281646,
+            4: 0.3951219505902449,
+        }
+        actual = simrank_similarity(G, source=0)
+        assert expected == pytest.approx(actual, abs=1e-2)
+
+        # For a DiGraph test, use the first graph from the paper cited in
+        # the docs: https://dl.acm.org/doi/pdf/10.1145/775047.775126
+        G = nx.DiGraph()
+        G.add_node(0, label="Univ")
+        G.add_node(1, label="ProfA")
+        G.add_node(2, label="ProfB")
+        G.add_node(3, label="StudentA")
+        G.add_node(4, label="StudentB")
+        G.add_edges_from([(0, 1), (0, 2), (1, 3), (2, 4), (4, 2), (3, 0)])
+
+        expected = {0: 1, 1: 0.0, 2: 0.1323363991265798, 3: 0.0, 4: 0.03387811817640443}
+        # Use the importance_factor from the paper to get the same numbers.
+        actual = simrank_similarity(G, importance_factor=0.8, source=0)
+        assert expected == pytest.approx(actual, abs=1e-2)
+
+    @pytest.mark.parametrize("simrank_similarity", simrank_algs)
+    def test_simrank_noninteger_nodes(self, simrank_similarity):
+        G = nx.cycle_graph(5)
+        G = nx.relabel_nodes(G, dict(enumerate("abcde")))
+        expected = {
+            "a": 1,
+            "b": 0.3951219505902448,
+            "c": 0.5707317069281646,
+            "d": 0.5707317069281646,
+            "e": 0.3951219505902449,
+        }
+        actual = simrank_similarity(G, source="a")
+        assert expected == pytest.approx(actual, abs=1e-2)
+
+        # For a DiGraph test, use the first graph from the paper cited in
+        # the docs: https://dl.acm.org/doi/pdf/10.1145/775047.775126
+        G = nx.DiGraph()
+        G.add_node(0, label="Univ")
+        G.add_node(1, label="ProfA")
+        G.add_node(2, label="ProfB")
+        G.add_node(3, label="StudentA")
+        G.add_node(4, label="StudentB")
+        G.add_edges_from([(0, 1), (0, 2), (1, 3), (2, 4), (4, 2), (3, 0)])
+        node_labels = dict(enumerate(nx.get_node_attributes(G, "label").values()))
+        G = nx.relabel_nodes(G, node_labels)
+
+        expected = {
+            "Univ": 1,
+            "ProfA": 0.0,
+            "ProfB": 0.1323363991265798,
+            "StudentA": 0.0,
+            "StudentB": 0.03387811817640443,
+        }
+        # Use the importance_factor from the paper to get the same numbers.
+        actual = simrank_similarity(G, importance_factor=0.8, source="Univ")
+        assert expected == pytest.approx(actual, abs=1e-2)
+
+    @pytest.mark.parametrize("simrank_similarity", simrank_algs)
+    def test_simrank_source_and_target(self, simrank_similarity):
+        G = nx.cycle_graph(5)
+        expected = 1
+        actual = simrank_similarity(G, source=0, target=0)
+        assert expected == pytest.approx(actual, abs=1e-2)
+
+        # For a DiGraph test, use the first graph from the paper cited in
+        # the docs: https://dl.acm.org/doi/pdf/10.1145/775047.775126
+        G = nx.DiGraph()
+        G.add_node(0, label="Univ")
+        G.add_node(1, label="ProfA")
+        G.add_node(2, label="ProfB")
+        G.add_node(3, label="StudentA")
+        G.add_node(4, label="StudentB")
+        G.add_edges_from([(0, 1), (0, 2), (1, 3), (2, 4), (4, 2), (3, 0)])
+
+        expected = 0.1323363991265798
+        # Use the importance_factor from the paper to get the same numbers.
+        # Use the pair (0,2) because (0,0) and (0,1) have trivial results.
+        actual = simrank_similarity(G, importance_factor=0.8, source=0, target=2)
+        assert expected == pytest.approx(actual, abs=1e-5)
+
+    @pytest.mark.parametrize("alg", simrank_algs)
+    def test_simrank_max_iterations(self, alg):
+        G = nx.cycle_graph(5)
+        pytest.raises(nx.ExceededMaxIterations, alg, G, max_iterations=10)
+
+    def test_simrank_source_not_found(self):
+        G = nx.cycle_graph(5)
+        with pytest.raises(nx.NodeNotFound, match="Source node 10 not in G"):
+            nx.simrank_similarity(G, source=10)
+
+    def test_simrank_target_not_found(self):
+        G = nx.cycle_graph(5)
+        with pytest.raises(nx.NodeNotFound, match="Target node 10 not in G"):
+            nx.simrank_similarity(G, target=10)
+
+    def test_simrank_between_versions(self):
+        G = nx.cycle_graph(5)
+        # _python tolerance 1e-4
+        expected_python_tol4 = {
+            0: 1,
+            1: 0.394512499239852,
+            2: 0.5703550452791322,
+            3: 0.5703550452791323,
+            4: 0.394512499239852,
+        }
+        # _numpy tolerance 1e-4
+        expected_numpy_tol4 = {
+            0: 1.0,
+            1: 0.3947180735764555,
+            2: 0.570482097206368,
+            3: 0.570482097206368,
+            4: 0.3947180735764555,
+        }
+        actual = nx.simrank_similarity(G, source=0)
+        assert expected_numpy_tol4 == pytest.approx(actual, abs=1e-7)
+        # versions differ at 1e-4 level but equal at 1e-3
+        assert expected_python_tol4 != pytest.approx(actual, abs=1e-4)
+        assert expected_python_tol4 == pytest.approx(actual, abs=1e-3)
+
+        actual = nx.similarity._simrank_similarity_python(G, source=0)
+        assert expected_python_tol4 == pytest.approx(actual, abs=1e-7)
+        # versions differ at 1e-4 level but equal at 1e-3
+        assert expected_numpy_tol4 != pytest.approx(actual, abs=1e-4)
+        assert expected_numpy_tol4 == pytest.approx(actual, abs=1e-3)
+
+    def test_simrank_numpy_no_source_no_target(self):
+        G = nx.cycle_graph(5)
+        expected = np.array(
+            [
+                [
+                    1.0,
+                    0.3947180735764555,
+                    0.570482097206368,
+                    0.570482097206368,
+                    0.3947180735764555,
+                ],
+                [
+                    0.3947180735764555,
+                    1.0,
+                    0.3947180735764555,
+                    0.570482097206368,
+                    0.570482097206368,
+                ],
+                [
+                    0.570482097206368,
+                    0.3947180735764555,
+                    1.0,
+                    0.3947180735764555,
+                    0.570482097206368,
+                ],
+                [
+                    0.570482097206368,
+                    0.570482097206368,
+                    0.3947180735764555,
+                    1.0,
+                    0.3947180735764555,
+                ],
+                [
+                    0.3947180735764555,
+                    0.570482097206368,
+                    0.570482097206368,
+                    0.3947180735764555,
+                    1.0,
+                ],
+            ]
+        )
+        actual = nx.similarity._simrank_similarity_numpy(G)
+        np.testing.assert_allclose(expected, actual, atol=1e-7)
+
+    def test_simrank_numpy_source_no_target(self):
+        G = nx.cycle_graph(5)
+        expected = np.array(
+            [
+                1.0,
+                0.3947180735764555,
+                0.570482097206368,
+                0.570482097206368,
+                0.3947180735764555,
+            ]
+        )
+        actual = nx.similarity._simrank_similarity_numpy(G, source=0)
+        np.testing.assert_allclose(expected, actual, atol=1e-7)
+
+    def test_simrank_numpy_source_and_target(self):
+        G = nx.cycle_graph(5)
+        expected = 1.0
+        actual = nx.similarity._simrank_similarity_numpy(G, source=0, target=0)
+        np.testing.assert_allclose(expected, actual, atol=1e-7)
+
+    def test_panther_similarity_unweighted(self):
+        np.random.seed(42)
+
+        G = nx.Graph()
+        G.add_edge(0, 1)
+        G.add_edge(0, 2)
+        G.add_edge(0, 3)
+        G.add_edge(1, 2)
+        G.add_edge(2, 4)
+        expected = {3: 0.5, 2: 0.5, 1: 0.5, 4: 0.125}
+        sim = nx.panther_similarity(G, 0, path_length=2)
+        assert sim == expected
+
+    def test_panther_similarity_weighted(self):
+        np.random.seed(42)
+
+        G = nx.Graph()
+        G.add_edge("v1", "v2", w=5)
+        G.add_edge("v1", "v3", w=1)
+        G.add_edge("v1", "v4", w=2)
+        G.add_edge("v2", "v3", w=0.1)
+        G.add_edge("v3", "v5", w=1)
+        expected = {"v3": 0.75, "v4": 0.5, "v2": 0.5, "v5": 0.25}
+        sim = nx.panther_similarity(G, "v1", path_length=2, weight="w")
+        assert sim == expected
+
+    def test_panther_similarity_source_not_found(self):
+        G = nx.Graph()
+        G.add_edges_from([(0, 1), (0, 2), (0, 3), (1, 2), (2, 4)])
+        with pytest.raises(nx.NodeNotFound, match="Source node 10 not in G"):
+            nx.panther_similarity(G, source=10)
+
+    def test_panther_similarity_isolated(self):
+        G = nx.Graph()
+        G.add_nodes_from(range(5))
+        with pytest.raises(
+            nx.NetworkXUnfeasible,
+            match="Panther similarity is not defined for the isolated source node 1.",
+        ):
+            nx.panther_similarity(G, source=1)
+
+    def test_generate_random_paths_unweighted(self):
+        index_map = {}
+        num_paths = 10
+        path_length = 2
+        G = nx.Graph()
+        G.add_edge(0, 1)
+        G.add_edge(0, 2)
+        G.add_edge(0, 3)
+        G.add_edge(1, 2)
+        G.add_edge(2, 4)
+        paths = nx.generate_random_paths(
+            G, num_paths, path_length=path_length, index_map=index_map, seed=42
+        )
+        expected_paths = [
+            [3, 0, 3],
+            [4, 2, 1],
+            [2, 1, 0],
+            [2, 0, 3],
+            [3, 0, 1],
+            [3, 0, 1],
+            [4, 2, 0],
+            [2, 1, 0],
+            [3, 0, 2],
+            [2, 1, 2],
+        ]
+        expected_map = {
+            0: {0, 2, 3, 4, 5, 6, 7, 8},
+            1: {1, 2, 4, 5, 7, 9},
+            2: {1, 2, 3, 6, 7, 8, 9},
+            3: {0, 3, 4, 5, 8},
+            4: {1, 6},
+        }
+
+        assert expected_paths == list(paths)
+        assert expected_map == index_map
+
+    def test_generate_random_paths_weighted(self):
+        np.random.seed(42)
+
+        index_map = {}
+        num_paths = 10
+        path_length = 6
+        G = nx.Graph()
+        G.add_edge("a", "b", weight=0.6)
+        G.add_edge("a", "c", weight=0.2)
+        G.add_edge("c", "d", weight=0.1)
+        G.add_edge("c", "e", weight=0.7)
+        G.add_edge("c", "f", weight=0.9)
+        G.add_edge("a", "d", weight=0.3)
+        paths = nx.generate_random_paths(
+            G, num_paths, path_length=path_length, index_map=index_map
+        )
+
+        expected_paths = [
+            ["d", "c", "f", "c", "d", "a", "b"],
+            ["e", "c", "f", "c", "f", "c", "e"],
+            ["d", "a", "b", "a", "b", "a", "c"],
+            ["b", "a", "d", "a", "b", "a", "b"],
+            ["d", "a", "b", "a", "b", "a", "d"],
+            ["d", "a", "b", "a", "b", "a", "c"],
+            ["d", "a", "b", "a", "b", "a", "b"],
+            ["f", "c", "f", "c", "f", "c", "e"],
+            ["d", "a", "d", "a", "b", "a", "b"],
+            ["e", "c", "f", "c", "e", "c", "d"],
+        ]
+        expected_map = {
+            "d": {0, 2, 3, 4, 5, 6, 8, 9},
+            "c": {0, 1, 2, 5, 7, 9},
+            "f": {0, 1, 9, 7},
+            "a": {0, 2, 3, 4, 5, 6, 8},
+            "b": {0, 2, 3, 4, 5, 6, 8},
+            "e": {1, 9, 7},
+        }
+
+        assert expected_paths == list(paths)
+        assert expected_map == index_map
+
+    def test_symmetry_with_custom_matching(self):
+        print("G2 is edge (a,b) and G3 is edge (a,a)")
+        print("but node order for G2 is (a,b) while for G3 it is (b,a)")
+
+        a, b = "A", "B"
+        G2 = nx.Graph()
+        G2.add_nodes_from((a, b))
+        G2.add_edges_from([(a, b)])
+        G3 = nx.Graph()
+        G3.add_nodes_from((b, a))
+        G3.add_edges_from([(a, a)])
+        for G in (G2, G3):
+            for n in G:
+                G.nodes[n]["attr"] = n
+            for e in G.edges:
+                G.edges[e]["attr"] = e
+        match = lambda x, y: x == y
+
+        print("Starting G2 to G3 GED calculation")
+        assert nx.graph_edit_distance(G2, G3, node_match=match, edge_match=match) == 1
+
+        print("Starting G3 to G2 GED calculation")
+        assert nx.graph_edit_distance(G3, G2, node_match=match, edge_match=match) == 1