GetClustCfAll (SWIG)

GetClustCfAll(Graph, DegToCCfV, SampleNodes=- 1)

Computes the average clustering coefficient, as well as the number of open and closed triads in the graph, as defined in Watts and Strogatz, Collective dynamics of ‘small-world’ networks.

Parameters:

• Graph: graph (input)

  A Snap.py graph or a network

• DegToCCfV: a vector of pairs of floats (output)

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

• SampleNodes: int (input)

  If !=-1 then compute clustering coefficient only for a random sample of SampleNodes nodes

Return value:

• list: [float, int, int]

  The list contains three elements: the average clustering coefficient, the number of closed triads, and the number of open triads in the graph.

For more info see: http://en.wikipedia.org/wiki/Watts_and_Strogatz_model

The following example shows how to compute the in degree for nodes in TNGraph, TUNGraph, and TNEANet:

import snap

Graph = snap.GenRndGnm(snap.PNGraph, 100, 1000)
DegToCCfV = snap.TFltPrV()
result = snap.GetClustCfAll(Graph, DegToCCfV)
for item in DegToCCfV:
    print("degree: %d, clustering coefficient: %f" % (item.GetVal1(), item.GetVal2()))
print("average clustering coefficient", result[0])
print("closed triads", result[1])
print("open triads", result[2])

Graph = snap.GenRndGnm(snap.PUNGraph, 100, 1000)
DegToCCfV = snap.TFltPrV()
result = snap.GetClustCfAll(Graph, DegToCCfV)
for item in DegToCCfV:
    print("degree: %d, clustering coefficient: %f" % (item.GetVal1(), item.GetVal2()))
print("average clustering coefficient", result[0])
print("closed triads", result[1])
print("open triads", result[2])

Graph = snap.GenRndGnm(snap.PNEANet, 100, 1000)
DegToCCfV = snap.TFltPrV()
result = snap.GetClustCfAll(Graph, DegToCCfV)
for item in DegToCCfV:
    print("degree: %d, clustering coefficient: %f" % (item.GetVal1(), item.GetVal2()))
print("average clustering coefficient", result[0])
print("closed triads", result[1])
print("open triads", result[2])