GetClustCf

GetClustCf(Graph, DegToCCfV, SampleNodes=-1)

Computes the distribution of average clustering coefficient. Considers the graph as undirected.

Parameters:

• Graph: graph (input)

  A Snap.py graph or a network.

• DegToCCfV: TFltPrV, a vector of float pairs (output)

  Vector of (degree, avg. clustering coefficient of nodes of that degree) pairs.

• SampleNodes: int (input)

  If !=-1 then compute clustering coefficient only for a random sample of SampleNodes nodes. Useful for approximate but quick computations.

Return value:

• float

  Average clustering coefficient over all node degrees.

The following example shows how to compute the clustering coefficient distribution in TNGraph, TUNGraph, and TNEANet:

import snap

Graph = snap.GenRndGnm(snap.PNGraph, 100, 1000)
CfVec = snap.TFltPrV()
Cf = snap.GetClustCf(Graph, CfVec, -1)
print "Average Clustering Coefficient: %f" % (Cf)
print "Coefficients by degree:\n"
for pair in CfVec:
    print "degree: %d, clustering coefficient: %f" % (pair.GetVal1(), pair.GetVal2())

UGraph = snap.GenRndGnm(snap.PUNGraph, 100, 1000)
CfVec = snap.TFltPrV()
Cf = snap.GetClustCf(UGraph, CfVec, -1)
print "Average Clustering Coefficient: %f" % (Cf)
print "Coefficients by degree:\n"
for pair in CfVec:
    print "degree: %d, clustering coefficient: %f" % (pair.GetVal1(), pair.GetVal2())

Network = snap.GenRndGnm(snap.PNEANet, 100, 1000)
CfVec = snap.TFltPrV()
Cf = snap.GetClustCf(Network, CfVec, -1)
print "Average Clustering Coefficient: %f" % (Cf)
print "Coefficients by degree:\n"
for pair in CfVec:
    print "degree: %d, clustering coefficient: %f" % (pair.GetVal1(), pair.GetVal2())