GetClustCf (SWIG)
'''''''''''''''''

.. function:: GetClustCf(Graph, DegToCCfV, SampleNodes=-1)
   :noindex:

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

Parameters:

- *Graph*: graph (input)
    A Snap.py graph or a network.

- *DegToCCfV*: :class:`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
:class:`TNGraph`, :class:`TUNGraph`, and :class:`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()))