def computePLDistribution(gr, interval):
clsv = algorithms.importance.closeness_centrality.getClosenessVectors(gr)
cls = []
for v in clsv.values():
cls.extend(v)
print min(cls)
return distribution.computeDistribution(cls, 0, max(cls), interval, len(cls))
def computeNodeBetweennessDistribution(gr):
numnodes = gr.getNumNodes()
nbs = algorithms.importance.betweenness_centrality.getNodeBetnSeq(gr)
return distribution.computeDistribution(nbs, 0, numnodes * (numnodes - 1), numnodes)
def computeEdgeBetweennessEntropy(gr):
numedges = gr.getNumEdges()
numnodes = gr.getNumNodes()
ebs = algorithms.importance.betweenness_centrality.getEdgeBetnSeq(gr)
ebd = distribution.computeDistribution(ebs, 0, numnodes * (numnodes - 1), numedges)
return entropy.computeEntropy(ebd)
def computeEdgeConnectivityDistribution(gr):
econ = robustness.robustness_measures.getConnSeq(gr)
return distribution.computeDistribution(econ, 0, max(econ), len(econ))
def computeOutdegreeDistributionEntropy(gr):
numnodes = gr.getNumNodes()
ds = algorithms.importance.degree_centrality.getOutDegSeq(gr)
dd = distribution.computeDistribution(ds, 0, numnodes - 1, numnodes)
return entropy.computeEntropy(dd)
def computeDegreeDistribution(gr):
numnodes = gr.getNumNodes()
ds = algorithms.importance.degree_centrality.getDegSeq(gr)
return distribution.computeDistribution(ds, 0, numnodes - 1, numnodes)