two version of R2R are hereHEADmaster

1 files changed, 642 insertions, 0 deletions

diff --git a/.venv/lib/python3.12/site-packages/networkx/algorithms/tests/test_summarization.py b/.venv/lib/python3.12/site-packages/networkx/algorithms/tests/test_summarization.py
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+"""
+Unit tests for dedensification and graph summarization
+"""
+
+import pytest
+
+import networkx as nx
+
+
+class TestDirectedDedensification:
+    def build_original_graph(self):
+        original_matrix = [
+            ("1", "BC"),
+            ("2", "ABC"),
+            ("3", ["A", "B", "6"]),
+            ("4", "ABC"),
+            ("5", "AB"),
+            ("6", ["5"]),
+            ("A", ["6"]),
+        ]
+        graph = nx.DiGraph()
+        for source, targets in original_matrix:
+            for target in targets:
+                graph.add_edge(source, target)
+        return graph
+
+    def build_compressed_graph(self):
+        compressed_matrix = [
+            ("1", "BC"),
+            ("2", ["ABC"]),
+            ("3", ["A", "B", "6"]),
+            ("4", ["ABC"]),
+            ("5", "AB"),
+            ("6", ["5"]),
+            ("A", ["6"]),
+            ("ABC", "ABC"),
+        ]
+        compressed_graph = nx.DiGraph()
+        for source, targets in compressed_matrix:
+            for target in targets:
+                compressed_graph.add_edge(source, target)
+        return compressed_graph
+
+    def test_empty(self):
+        """
+        Verify that an empty directed graph results in no compressor nodes
+        """
+        G = nx.DiGraph()
+        compressed_graph, c_nodes = nx.dedensify(G, threshold=2)
+        assert c_nodes == set()
+
+    @staticmethod
+    def densify(G, compressor_nodes, copy=True):
+        """
+        Reconstructs the original graph from a dedensified, directed graph
+
+        Parameters
+        ----------
+        G: dedensified graph
+           A networkx graph
+        compressor_nodes: iterable
+           Iterable of compressor nodes in the dedensified graph
+        inplace: bool, optional (default: False)
+           Indicates if densification should be done inplace
+
+        Returns
+        -------
+        G: graph
+           A densified networkx graph
+        """
+        if copy:
+            G = G.copy()
+        for compressor_node in compressor_nodes:
+            all_neighbors = set(nx.all_neighbors(G, compressor_node))
+            out_neighbors = set(G.neighbors(compressor_node))
+            for out_neighbor in out_neighbors:
+                G.remove_edge(compressor_node, out_neighbor)
+            in_neighbors = all_neighbors - out_neighbors
+            for in_neighbor in in_neighbors:
+                G.remove_edge(in_neighbor, compressor_node)
+                for out_neighbor in out_neighbors:
+                    G.add_edge(in_neighbor, out_neighbor)
+            G.remove_node(compressor_node)
+        return G
+
+    def setup_method(self):
+        self.c_nodes = ("ABC",)
+
+    def test_dedensify_edges(self):
+        """
+        Verifies that dedensify produced the correct edges to/from compressor
+        nodes in a directed graph
+        """
+        G = self.build_original_graph()
+        compressed_G = self.build_compressed_graph()
+        compressed_graph, c_nodes = nx.dedensify(G, threshold=2)
+        for s, t in compressed_graph.edges():
+            o_s = "".join(sorted(s))
+            o_t = "".join(sorted(t))
+            compressed_graph_exists = compressed_graph.has_edge(s, t)
+            verified_compressed_exists = compressed_G.has_edge(o_s, o_t)
+            assert compressed_graph_exists == verified_compressed_exists
+        assert len(c_nodes) == len(self.c_nodes)
+
+    def test_dedensify_edge_count(self):
+        """
+        Verifies that dedensify produced the correct number of compressor nodes
+        in a directed graph
+        """
+        G = self.build_original_graph()
+        original_edge_count = len(G.edges())
+        c_G, c_nodes = nx.dedensify(G, threshold=2)
+        compressed_edge_count = len(c_G.edges())
+        assert compressed_edge_count <= original_edge_count
+        compressed_G = self.build_compressed_graph()
+        assert compressed_edge_count == len(compressed_G.edges())
+
+    def test_densify_edges(self):
+        """
+        Verifies that densification produces the correct edges from the
+        original directed graph
+        """
+        compressed_G = self.build_compressed_graph()
+        original_graph = self.densify(compressed_G, self.c_nodes, copy=True)
+        G = self.build_original_graph()
+        for s, t in G.edges():
+            assert G.has_edge(s, t) == original_graph.has_edge(s, t)
+
+    def test_densify_edge_count(self):
+        """
+        Verifies that densification produces the correct number of edges in the
+        original directed graph
+        """
+        compressed_G = self.build_compressed_graph()
+        compressed_edge_count = len(compressed_G.edges())
+        original_graph = self.densify(compressed_G, self.c_nodes)
+        original_edge_count = len(original_graph.edges())
+        assert compressed_edge_count <= original_edge_count
+        G = self.build_original_graph()
+        assert original_edge_count == len(G.edges())
+
+
+class TestUnDirectedDedensification:
+    def build_original_graph(self):
+        """
+        Builds graph shown in the original research paper
+        """
+        original_matrix = [
+            ("1", "CB"),
+            ("2", "ABC"),
+            ("3", ["A", "B", "6"]),
+            ("4", "ABC"),
+            ("5", "AB"),
+            ("6", ["5"]),
+            ("A", ["6"]),
+        ]
+        graph = nx.Graph()
+        for source, targets in original_matrix:
+            for target in targets:
+                graph.add_edge(source, target)
+        return graph
+
+    def test_empty(self):
+        """
+        Verify that an empty undirected graph results in no compressor nodes
+        """
+        G = nx.Graph()
+        compressed_G, c_nodes = nx.dedensify(G, threshold=2)
+        assert c_nodes == set()
+
+    def setup_method(self):
+        self.c_nodes = ("6AB", "ABC")
+
+    def build_compressed_graph(self):
+        compressed_matrix = [
+            ("1", ["B", "C"]),
+            ("2", ["ABC"]),
+            ("3", ["6AB"]),
+            ("4", ["ABC"]),
+            ("5", ["6AB"]),
+            ("6", ["6AB", "A"]),
+            ("A", ["6AB", "ABC"]),
+            ("B", ["ABC", "6AB"]),
+            ("C", ["ABC"]),
+        ]
+        compressed_graph = nx.Graph()
+        for source, targets in compressed_matrix:
+            for target in targets:
+                compressed_graph.add_edge(source, target)
+        return compressed_graph
+
+    def test_dedensify_edges(self):
+        """
+        Verifies that dedensify produced correct compressor nodes and the
+        correct edges to/from the compressor nodes in an undirected graph
+        """
+        G = self.build_original_graph()
+        c_G, c_nodes = nx.dedensify(G, threshold=2)
+        v_compressed_G = self.build_compressed_graph()
+        for s, t in c_G.edges():
+            o_s = "".join(sorted(s))
+            o_t = "".join(sorted(t))
+            has_compressed_edge = c_G.has_edge(s, t)
+            verified_has_compressed_edge = v_compressed_G.has_edge(o_s, o_t)
+            assert has_compressed_edge == verified_has_compressed_edge
+        assert len(c_nodes) == len(self.c_nodes)
+
+    def test_dedensify_edge_count(self):
+        """
+        Verifies that dedensify produced the correct number of edges in an
+        undirected graph
+        """
+        G = self.build_original_graph()
+        c_G, c_nodes = nx.dedensify(G, threshold=2, copy=True)
+        compressed_edge_count = len(c_G.edges())
+        verified_original_edge_count = len(G.edges())
+        assert compressed_edge_count <= verified_original_edge_count
+        verified_compressed_G = self.build_compressed_graph()
+        verified_compressed_edge_count = len(verified_compressed_G.edges())
+        assert compressed_edge_count == verified_compressed_edge_count
+
+
+@pytest.mark.parametrize(
+    "graph_type", [nx.Graph, nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph]
+)
+def test_summarization_empty(graph_type):
+    G = graph_type()
+    summary_graph = nx.snap_aggregation(G, node_attributes=("color",))
+    assert nx.is_isomorphic(summary_graph, G)
+
+
+class AbstractSNAP:
+    node_attributes = ("color",)
+
+    def build_original_graph(self):
+        pass
+
+    def build_summary_graph(self):
+        pass
+
+    def test_summary_graph(self):
+        original_graph = self.build_original_graph()
+        summary_graph = self.build_summary_graph()
+
+        relationship_attributes = ("type",)
+        generated_summary_graph = nx.snap_aggregation(
+            original_graph, self.node_attributes, relationship_attributes
+        )
+        relabeled_summary_graph = self.deterministic_labels(generated_summary_graph)
+        assert nx.is_isomorphic(summary_graph, relabeled_summary_graph)
+
+    def deterministic_labels(self, G):
+        node_labels = list(G.nodes)
+        node_labels = sorted(node_labels, key=lambda n: sorted(G.nodes[n]["group"])[0])
+        node_labels.sort()
+
+        label_mapping = {}
+        for index, node in enumerate(node_labels):
+            label = f"Supernode-{index}"
+            label_mapping[node] = label
+
+        return nx.relabel_nodes(G, label_mapping)
+
+
+class TestSNAPNoEdgeTypes(AbstractSNAP):
+    relationship_attributes = ()
+
+    def test_summary_graph(self):
+        original_graph = self.build_original_graph()
+        summary_graph = self.build_summary_graph()
+
+        relationship_attributes = ("type",)
+        generated_summary_graph = nx.snap_aggregation(
+            original_graph, self.node_attributes
+        )
+        relabeled_summary_graph = self.deterministic_labels(generated_summary_graph)
+        assert nx.is_isomorphic(summary_graph, relabeled_summary_graph)
+
+    def build_original_graph(self):
+        nodes = {
+            "A": {"color": "Red"},
+            "B": {"color": "Red"},
+            "C": {"color": "Red"},
+            "D": {"color": "Red"},
+            "E": {"color": "Blue"},
+            "F": {"color": "Blue"},
+            "G": {"color": "Blue"},
+            "H": {"color": "Blue"},
+            "I": {"color": "Yellow"},
+            "J": {"color": "Yellow"},
+            "K": {"color": "Yellow"},
+            "L": {"color": "Yellow"},
+        }
+        edges = [
+            ("A", "B"),
+            ("A", "C"),
+            ("A", "E"),
+            ("A", "I"),
+            ("B", "D"),
+            ("B", "J"),
+            ("B", "F"),
+            ("C", "G"),
+            ("D", "H"),
+            ("I", "J"),
+            ("J", "K"),
+            ("I", "L"),
+        ]
+        G = nx.Graph()
+        for node in nodes:
+            attributes = nodes[node]
+            G.add_node(node, **attributes)
+
+        for source, target in edges:
+            G.add_edge(source, target)
+
+        return G
+
+    def build_summary_graph(self):
+        nodes = {
+            "Supernode-0": {"color": "Red"},
+            "Supernode-1": {"color": "Red"},
+            "Supernode-2": {"color": "Blue"},
+            "Supernode-3": {"color": "Blue"},
+            "Supernode-4": {"color": "Yellow"},
+            "Supernode-5": {"color": "Yellow"},
+        }
+        edges = [
+            ("Supernode-0", "Supernode-0"),
+            ("Supernode-0", "Supernode-1"),
+            ("Supernode-0", "Supernode-2"),
+            ("Supernode-0", "Supernode-4"),
+            ("Supernode-1", "Supernode-3"),
+            ("Supernode-4", "Supernode-4"),
+            ("Supernode-4", "Supernode-5"),
+        ]
+        G = nx.Graph()
+        for node in nodes:
+            attributes = nodes[node]
+            G.add_node(node, **attributes)
+
+        for source, target in edges:
+            G.add_edge(source, target)
+
+        supernodes = {
+            "Supernode-0": {"A", "B"},
+            "Supernode-1": {"C", "D"},
+            "Supernode-2": {"E", "F"},
+            "Supernode-3": {"G", "H"},
+            "Supernode-4": {"I", "J"},
+            "Supernode-5": {"K", "L"},
+        }
+        nx.set_node_attributes(G, supernodes, "group")
+        return G
+
+
+class TestSNAPUndirected(AbstractSNAP):
+    def build_original_graph(self):
+        nodes = {
+            "A": {"color": "Red"},
+            "B": {"color": "Red"},
+            "C": {"color": "Red"},
+            "D": {"color": "Red"},
+            "E": {"color": "Blue"},
+            "F": {"color": "Blue"},
+            "G": {"color": "Blue"},
+            "H": {"color": "Blue"},
+            "I": {"color": "Yellow"},
+            "J": {"color": "Yellow"},
+            "K": {"color": "Yellow"},
+            "L": {"color": "Yellow"},
+        }
+        edges = [
+            ("A", "B", "Strong"),
+            ("A", "C", "Weak"),
+            ("A", "E", "Strong"),
+            ("A", "I", "Weak"),
+            ("B", "D", "Weak"),
+            ("B", "J", "Weak"),
+            ("B", "F", "Strong"),
+            ("C", "G", "Weak"),
+            ("D", "H", "Weak"),
+            ("I", "J", "Strong"),
+            ("J", "K", "Strong"),
+            ("I", "L", "Strong"),
+        ]
+        G = nx.Graph()
+        for node in nodes:
+            attributes = nodes[node]
+            G.add_node(node, **attributes)
+
+        for source, target, type in edges:
+            G.add_edge(source, target, type=type)
+
+        return G
+
+    def build_summary_graph(self):
+        nodes = {
+            "Supernode-0": {"color": "Red"},
+            "Supernode-1": {"color": "Red"},
+            "Supernode-2": {"color": "Blue"},
+            "Supernode-3": {"color": "Blue"},
+            "Supernode-4": {"color": "Yellow"},
+            "Supernode-5": {"color": "Yellow"},
+        }
+        edges = [
+            ("Supernode-0", "Supernode-0", "Strong"),
+            ("Supernode-0", "Supernode-1", "Weak"),
+            ("Supernode-0", "Supernode-2", "Strong"),
+            ("Supernode-0", "Supernode-4", "Weak"),
+            ("Supernode-1", "Supernode-3", "Weak"),
+            ("Supernode-4", "Supernode-4", "Strong"),
+            ("Supernode-4", "Supernode-5", "Strong"),
+        ]
+        G = nx.Graph()
+        for node in nodes:
+            attributes = nodes[node]
+            G.add_node(node, **attributes)
+
+        for source, target, type in edges:
+            G.add_edge(source, target, types=[{"type": type}])
+
+        supernodes = {
+            "Supernode-0": {"A", "B"},
+            "Supernode-1": {"C", "D"},
+            "Supernode-2": {"E", "F"},
+            "Supernode-3": {"G", "H"},
+            "Supernode-4": {"I", "J"},
+            "Supernode-5": {"K", "L"},
+        }
+        nx.set_node_attributes(G, supernodes, "group")
+        return G
+
+
+class TestSNAPDirected(AbstractSNAP):
+    def build_original_graph(self):
+        nodes = {
+            "A": {"color": "Red"},
+            "B": {"color": "Red"},
+            "C": {"color": "Green"},
+            "D": {"color": "Green"},
+            "E": {"color": "Blue"},
+            "F": {"color": "Blue"},
+            "G": {"color": "Yellow"},
+            "H": {"color": "Yellow"},
+        }
+        edges = [
+            ("A", "C", "Strong"),
+            ("A", "E", "Strong"),
+            ("A", "F", "Weak"),
+            ("B", "D", "Strong"),
+            ("B", "E", "Weak"),
+            ("B", "F", "Strong"),
+            ("C", "G", "Strong"),
+            ("C", "F", "Strong"),
+            ("D", "E", "Strong"),
+            ("D", "H", "Strong"),
+            ("G", "E", "Strong"),
+            ("H", "F", "Strong"),
+        ]
+        G = nx.DiGraph()
+        for node in nodes:
+            attributes = nodes[node]
+            G.add_node(node, **attributes)
+
+        for source, target, type in edges:
+            G.add_edge(source, target, type=type)
+
+        return G
+
+    def build_summary_graph(self):
+        nodes = {
+            "Supernode-0": {"color": "Red"},
+            "Supernode-1": {"color": "Green"},
+            "Supernode-2": {"color": "Blue"},
+            "Supernode-3": {"color": "Yellow"},
+        }
+        edges = [
+            ("Supernode-0", "Supernode-1", [{"type": "Strong"}]),
+            ("Supernode-0", "Supernode-2", [{"type": "Weak"}, {"type": "Strong"}]),
+            ("Supernode-1", "Supernode-2", [{"type": "Strong"}]),
+            ("Supernode-1", "Supernode-3", [{"type": "Strong"}]),
+            ("Supernode-3", "Supernode-2", [{"type": "Strong"}]),
+        ]
+        G = nx.DiGraph()
+        for node in nodes:
+            attributes = nodes[node]
+            G.add_node(node, **attributes)
+
+        for source, target, types in edges:
+            G.add_edge(source, target, types=types)
+
+        supernodes = {
+            "Supernode-0": {"A", "B"},
+            "Supernode-1": {"C", "D"},
+            "Supernode-2": {"E", "F"},
+            "Supernode-3": {"G", "H"},
+            "Supernode-4": {"I", "J"},
+            "Supernode-5": {"K", "L"},
+        }
+        nx.set_node_attributes(G, supernodes, "group")
+        return G
+
+
+class TestSNAPUndirectedMulti(AbstractSNAP):
+    def build_original_graph(self):
+        nodes = {
+            "A": {"color": "Red"},
+            "B": {"color": "Red"},
+            "C": {"color": "Red"},
+            "D": {"color": "Blue"},
+            "E": {"color": "Blue"},
+            "F": {"color": "Blue"},
+            "G": {"color": "Yellow"},
+            "H": {"color": "Yellow"},
+            "I": {"color": "Yellow"},
+        }
+        edges = [
+            ("A", "D", ["Weak", "Strong"]),
+            ("B", "E", ["Weak", "Strong"]),
+            ("D", "I", ["Strong"]),
+            ("E", "H", ["Strong"]),
+            ("F", "G", ["Weak"]),
+            ("I", "G", ["Weak", "Strong"]),
+            ("I", "H", ["Weak", "Strong"]),
+            ("G", "H", ["Weak", "Strong"]),
+        ]
+        G = nx.MultiGraph()
+        for node in nodes:
+            attributes = nodes[node]
+            G.add_node(node, **attributes)
+
+        for source, target, types in edges:
+            for type in types:
+                G.add_edge(source, target, type=type)
+
+        return G
+
+    def build_summary_graph(self):
+        nodes = {
+            "Supernode-0": {"color": "Red"},
+            "Supernode-1": {"color": "Blue"},
+            "Supernode-2": {"color": "Yellow"},
+            "Supernode-3": {"color": "Blue"},
+            "Supernode-4": {"color": "Yellow"},
+            "Supernode-5": {"color": "Red"},
+        }
+        edges = [
+            ("Supernode-1", "Supernode-2", [{"type": "Weak"}]),
+            ("Supernode-2", "Supernode-4", [{"type": "Weak"}, {"type": "Strong"}]),
+            ("Supernode-3", "Supernode-4", [{"type": "Strong"}]),
+            ("Supernode-3", "Supernode-5", [{"type": "Weak"}, {"type": "Strong"}]),
+            ("Supernode-4", "Supernode-4", [{"type": "Weak"}, {"type": "Strong"}]),
+        ]
+        G = nx.MultiGraph()
+        for node in nodes:
+            attributes = nodes[node]
+            G.add_node(node, **attributes)
+
+        for source, target, types in edges:
+            for type in types:
+                G.add_edge(source, target, type=type)
+
+        supernodes = {
+            "Supernode-0": {"A", "B"},
+            "Supernode-1": {"C", "D"},
+            "Supernode-2": {"E", "F"},
+            "Supernode-3": {"G", "H"},
+            "Supernode-4": {"I", "J"},
+            "Supernode-5": {"K", "L"},
+        }
+        nx.set_node_attributes(G, supernodes, "group")
+        return G
+
+
+class TestSNAPDirectedMulti(AbstractSNAP):
+    def build_original_graph(self):
+        nodes = {
+            "A": {"color": "Red"},
+            "B": {"color": "Red"},
+            "C": {"color": "Green"},
+            "D": {"color": "Green"},
+            "E": {"color": "Blue"},
+            "F": {"color": "Blue"},
+            "G": {"color": "Yellow"},
+            "H": {"color": "Yellow"},
+        }
+        edges = [
+            ("A", "C", ["Weak", "Strong"]),
+            ("A", "E", ["Strong"]),
+            ("A", "F", ["Weak"]),
+            ("B", "D", ["Weak", "Strong"]),
+            ("B", "E", ["Weak"]),
+            ("B", "F", ["Strong"]),
+            ("C", "G", ["Weak", "Strong"]),
+            ("C", "F", ["Strong"]),
+            ("D", "E", ["Strong"]),
+            ("D", "H", ["Weak", "Strong"]),
+            ("G", "E", ["Strong"]),
+            ("H", "F", ["Strong"]),
+        ]
+        G = nx.MultiDiGraph()
+        for node in nodes:
+            attributes = nodes[node]
+            G.add_node(node, **attributes)
+
+        for source, target, types in edges:
+            for type in types:
+                G.add_edge(source, target, type=type)
+
+        return G
+
+    def build_summary_graph(self):
+        nodes = {
+            "Supernode-0": {"color": "Red"},
+            "Supernode-1": {"color": "Blue"},
+            "Supernode-2": {"color": "Yellow"},
+            "Supernode-3": {"color": "Blue"},
+        }
+        edges = [
+            ("Supernode-0", "Supernode-1", ["Weak", "Strong"]),
+            ("Supernode-0", "Supernode-2", ["Weak", "Strong"]),
+            ("Supernode-1", "Supernode-2", ["Strong"]),
+            ("Supernode-1", "Supernode-3", ["Weak", "Strong"]),
+            ("Supernode-3", "Supernode-2", ["Strong"]),
+        ]
+        G = nx.MultiDiGraph()
+        for node in nodes:
+            attributes = nodes[node]
+            G.add_node(node, **attributes)
+
+        for source, target, types in edges:
+            for type in types:
+                G.add_edge(source, target, type=type)
+
+        supernodes = {
+            "Supernode-0": {"A", "B"},
+            "Supernode-1": {"C", "D"},
+            "Supernode-2": {"E", "F"},
+            "Supernode-3": {"G", "H"},
+        }
+        nx.set_node_attributes(G, supernodes, "group")
+        return G