Hi Tiago, Thank you for your detailed reply, it cleared things up a lot! I would also like to thank you for your incredible work on graph-tool, its a great package! Below is the code i now use to count the triangles - # number of triangles gc = global_clustering(tempG) d = tempG.degree_property_map("total") num_triangles = gc[0] * (d.a * (d.a - 1) / 2).sum() / 3 My test dataset is the CA-HepPh network from SNAP - http://snap.stanford.edu/data/ca-HepPh.html However i am not getting the reported number of triangles for the dataset which, according to SNAP, is - 3,358,499 However from the above graph-tool code i get - 13,434,795 Do you have any idea what might cause the discrepancy between the ground truth and the computed result? Thanks again, Stephen Bonner
On 2 Apr 2016, at 15:39, Tiago de Paula Peixoto <tiago@skewed.de> wrote:
On 01.04.2016 15:51, Stephen Bonner wrote:
Is there a primitive function for counting triangles in graph-tool?
The closest one is global_clustering(), which returns:
3 x number of triangles / number of connected triples
the denominator is easy to compute from the degrees alone (the number of triples a node with degree k participates is simply k(k-1)/2). Hence, you can get the number of triangles with:
d = g.degree_property_map("out") n_t = global_clustering(g)[0] * (d.a * (d.a - 1) / 2) .sum() / 3
Currently i am explicitly creating a triangle graph and then using the motif function to search for this graph. -
triangle = Graph() v1 = triangle.add_vertex() v2 = triangle.add_vertex() v3 = triangle.add_vertex() e = triangle.add_edge(v1, v2) e = triangle.add_edge(v1, v3) e = triangle.add_edge(v3, v2) triangle.set_directed(False)
motifsgraph, num_triangles = motifs(tempG, 3, motif_list=[triangle])
Just wondering if there is a better way to do this as it is 1) slow and 2) turning up some strange results.
This should work too. What strange results are you seeing?
Best, Tiago
-- Tiago de Paula Peixoto <tiago@skewed.de>
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