CommunityCNM (SWIG)
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.. function:: CommunityCNM (Graph, CmtyV)
   :noindex:

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 :meth:`DelSelfEdges`.

- *CmtyV*: :class:`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 :class:`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)