GetEigenVectorCentr(Graph, NIdEigenH, Eps = 1e-4, MaxIter = 100)

Computes eigenvector centrality of all nodes in Graph and stores it in NIdEigenH. Eigenvector Centrality of a node N is defined recursively as the average of centrality values of N’s neighbors in the network.


  • Graph: undirected graph (input)

    A undirected graph

  • NIdEigenH: TIntFltH, a hash table of int keys and float values (output)

    Hash table mapping node ids to their corresponding eigenvector centrality values.

  • Eps: float (input)

    Epsilon (stop when accumulated difference in eigenvector centrality value for all nodes in an iteration is less than epsilon).

  • MaxIter: int (input)

    Maximum number of iterations (stop when exceeding this number of iterations).

Return value:

  • None

The following example shows how to calculate eigenvector centrality values for nodes in TUNGraph:

import snap

UGraph = snap.GenRndGnm(snap.PUNGraph, 100, 1000)
NIdEigenH = snap.TIntFltH()
snap.GetEigenVectorCentr(UGraph, NIdEigenH)
for item in NIdEigenH:
    print "%node: d centrality: %f" % (item, NIdEigenH[item])

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