CommunityCNM (SWIG)ΒΆ

CommunityCNM(Graph, CmtyV)

Uses the Clauset-Newman-Moore community detection method for large networks. At every step of the algorithm two communities that contribute maximum positive value to global modularity are merged. Fills CmtyV with all the communities detected and returns the modularity of the network.

Parameters:

  • Graph: undirected graph (input)

    A Snap.py undirected graph. Make sure that Graph has no self-edges. If needed, use DelSelfEdges().

  • CmtyV: TCnComV, a vector of connected components (output)

    A vector of all the communities that are detected by the CNM method. Each community is represented as a vector of node IDs.

Return value:

  • float

    The modularity of the network.

The following example shows how to detect communities using CNM algorithm in TUNGraph:

import snap

UGraph = snap.GenRndGnm(snap.PUNGraph, 100, 1000)
CmtyV = snap.TCnComV()
modularity = snap.CommunityCNM(UGraph, CmtyV)
for Cmty in CmtyV:
    print("Community: ")
    for NI in Cmty:
        print(NI)
print("The modularity of the network is %f" % modularity)