ran over the graph algorithms conversion and made corrections

jakub-borusewicz · May 6, 2020 · b816ad0 · b816ad0
1 parent 1a5afb4
commit b816ad0
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diff --git a/examples/cypher.py b/examples/cypher.py
@@ -32,23  32,5 @@ def cypher():
     print(nxneo4j.betweenness_centrality(G))
     print(nxneo4j.closeness_centrality(G))
 
-    config = {
-        "node_label": """\
-        MATCH (p:Person) RETURN id(p) AS id
-        """,
-        "relationship_type": """\
-        MATCH (p1:Person)-[:FRIENDS]-(p2:Person)
-        RETURN id(p1) AS source, id(p2) AS target
-        """,
-        "identifier_property": "name",
-        "graph": "cypher"
-    }
-
-    G = nxneo4j.Graph(driver, config)
-
-    print(nxneo4j.betweenness_centrality(G))
-    print(nxneo4j.closeness_centrality(G))
-
-
 if __name__ == '__main__':
     cypher()
diff --git a/examples/networkx_vs_neo4j.py b/examples/networkx_vs_neo4j.py
@@ -9,11  9,8 @@
     "pagerank": nx.pagerank,
     "triangles": nx.triangles,
     "clustering": nx.clustering,
-    "average_clustering": nx.average_clustering,
     "label_propagation_communities": nx.algorithms.community.label_propagation_communities,
     "shortest_path": nx.shortest_path,
-    "connected_components": nx.connected_components,
-    "number_connected_components": nx.number_connected_components
 }
 
 neo4j_functions = {
@@ -22,11  19,8 @@
     "pagerank": nxneo4j.pagerank,
     "triangles": nxneo4j.triangles,
     "clustering": nxneo4j.clustering,
-    "average_clustering": nxneo4j.average_clustering,
     "label_propagation_communities": nxneo4j.community.label_propagation_communities,
     "shortest_path": nxneo4j.shortest_path,
-    "connected_components": nxneo4j.connected_components,
-    "number_connected_components": nxneo4j.number_connected_components
 }
 
 driver = GraphDatabase.driver(uri="bolt://localhost",auth=("neo4j","neo"))
@@ -60,24  54,12 @@ def execute_graph(G, functions):
     clustering = functions["clustering"]
     print("Clustering Coefficient: {0}".format(clustering(G)))
 
-    average_clustering = functions["average_clustering"]
-    print("Average Clustering Coefficient: {0}".format(average_clustering(G)))
-
     lpa = functions["label_propagation_communities"]
     print("Label Propagation: {0}".format(list(lpa(G))))
 
     shortest_path = functions["shortest_path"]
-    print("Shortest Path: {0}".format(shortest_path(G, 1, 5, 'weight')))
     print("Single Shortest Path: {0}".format(shortest_path(G, 1)))
 
-    connected_components = functions["connected_components"]
-    print("Connected Components: {0}".format(list(connected_components(G))))
-
-    number_connected_components = functions["number_connected_components"]
-    print("# Connected Components: {0}".format(number_connected_components(G)))
-
-
-
 if __name__ == '__main__':
     print("Neo4j")
     execute_graph(nxneo4j.Graph(GraphDatabase.driver("bolt://localhost", auth=basic_auth("neo4j", "neo"))),

diff --git a/nxneo4j/base_graph.py b/nxneo4j/base_graph.py
@@ -11,7  11,7 @@ def __init__(self, driver, direction, config=None):
         self.identifier_property = config.get("identifier_property", "id")
 
     add_node_query = """\
-    MERGE (:`%s` {`%s`: $value })
     MERGE (:`%s` {`%s`:  $value })
     """
 
     def base_params(self):
@@ -114,16  114,15 @@ def betweenness_centrality(self):
             orientation: $direction,
             properties: {}
         }
-    })
     }})
     YIELD nodeId, centrality
     RETURN gds.util.asNode(nodeId).`%s` AS node, centrality
     ORDER BY centrality DESC, node ASC
     """
 
-    def closeness_centrality(self, wf_improved=True):
     def closeness_centrality(self,wf_improved=True):
         with self.driver.session() as session:
             params = self.base_params()
-            params["wfImproved"] = wf_improved
             query = self.closeness_centrality_query % self.identifier_property
 
             result = {row["node"]: row["centrality"] for row in session.run(query, params)}
@@ -187,31  186,6 @@ def clustering(self):
             result = {row["node"]: row["coefficient"] for row in session.run(query, params)}
         return result
 
-    triangle_query = """\
-    CALL gds.alpha.triangleCount.stream({
-        nodeProjection: $nodeLabel,
-        relationshipProjection: {
-            relType: {
-                type: $relationshipType,
-                orientation: $direction,
-                properties: {}
-            }
-        },
-        clusteringCoefficientProperty: 'clusteringCoefficient'
-    })
-    YIELD nodeId, triangles, coefficient
-    WITH gds.util.asNode(nodeId) AS node, coefficient, triangles
-    RETURN node, triangles, coefficient
-    ORDER BY triangles DESC
-    """
-
-    def average_clustering(self):
-        with self.driver.session() as session:
-            params = self.base_params()
-            query = self.triangle_query % (self.relationship_type)
-            result = session.run(query,params)
-            result.peek()["clusteringCoefficient"]
-
     lpa_query = """\
     CALL gds.labelPropagation.stream({
         nodeProjection: $nodeLabel,
@@ -225,9  199,8 @@ def average_clustering(self):
         relationshipWeightProperty: null
     })
     YIELD nodeId, communityId AS community
-    WITH gds.util.asNode(nodeId).`%s` AS node, community
-    RETURN node, community
-    ORDER BY community DESC
     MATCH (n) WHERE id(n) = nodeId
     RETURN community, collect(n.`%s`) AS nodes
     """
 
     def label_propagation(self):
@@ -248,12  221,12 @@ def label_propagation(self):
             relType: {
                 type: $relationshipType,
                 orientation: $direction,
-                properties: {$propertyName}
                 properties: {}
             }
         },
         startNode: source,
         endNode: target,
-        relationshipWeightProperty: null
         relationshipWeightProperty: $propertyName
     })
     YIELD nodeId, cost
     RETURN gds.util.asNode(nodeId).`%s` AS node, cost