def get_value(self, graph: Graph):
self.logger.debug('calculate number of triangles')
graph.indexEdges()
triangles = TriangleEdgeScore(graph).run().scores()
res = []
for item in self.process_centrality(triangles):
res.append(float(item))
return res
def scores(self, G):
""" Returns an edge attribute that holds for each edge the minimum parameter value
such that the edge is contained in the sparsified graph.
Keyword arguments:
G -- the input graph
"""
triangles = TriangleEdgeScore(G).run().scores()
localSimScore = LocalSimilarityScore(G, triangles)
localSimScore.run()
return localSimScore.scores()
def scores(self, G):
""" Returns an edge attribute that holds for each edge the minimum jaccard filter value
such that the edge is contained in the sparsified graph.
Keyword arguments:
G -- the input graph
"""
triangles = TriangleEdgeScore(G).run().scores()
a_sj = PrefixJaccardScore(G, triangles).run().scores()
return a_sj
def scores(self, G):
""" Returns an edge attribute that holds for each edge the maximum parameter value
such that the edge is contained in the sparsified graph.
Keyword arguments:
G -- the input graph
"""
triangles = TriangleEdgeScore(G).run().scores()
ms = MultiscaleScore(G, triangles)
ms.run()
a_ms = ms.scores()
return a_ms
def scores(self, G):
""" Returns an edge attribute that holds for each edge the minimum parameter value
such that the edge is contained in the sparsified graph.
Keyword arguments:
G -- the input graph
"""
triangles = TriangleEdgeScore(G).run().scores()
simmelianOverlap = SimmelianOverlapScore(G, triangles, self.maxRank)
simmelianOverlap.run()
return simmelianOverlap.scores()
def scores(self, G):
""" Returns the triangle counts of the input graph. """
triangleScore = TriangleEdgeScore(G)
triangleScore.run()
return triangleScore.scores()
def scores(self, G):
""" Returns the jaccard coefficient of the neighborhoods of the two incident nodes """
triangles = TriangleEdgeScore(G).run().scores()
return JaccardSimilarityAttributizer(G, triangles).getAttribute()
def scores(self, G): """ Returns the triangle counts of the input graph. """ triangleScore = TriangleEdgeScore(G) triangleScore.run() return triangleScore.scores()