GetBetweennessCentr

GetBetweennessCentr(NodeFrac=1.0, IsDir=False)

A graph method that computes (approximate) Node and Edge Betweenness Centrality based on a sample of NodeFrac nodes.

Parameters:

• (optional) NodeFrac: float

  Quality of the approximation. NodeFrac=1.0 gives exact betweenness values.

• (optional) IsDir: bool

  Indicates whether the edges should be considered directed (True) or undirected (False).

Return Value:

• NIdBtwH: TIntFltH, a hash table with int keys and float values

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

• EdgeBtwH: TIntPrFltH, a hash table with int pair keys and float values

  Hash table mapping edges (provided as pairs of node ids) to their corresponding betweenness centrality values.

See “A Faster Algorithm for Betweenness Centrality”, Ulrik Brandes, Journal of Mathematical Sociology, 2001, and “Centrality Estimation in Large Networks”, Urlik Brandes and Christian Pich, 2006 for more details.

The following example shows how to calculate Betweenness Centrality scores for nodes and edges in TNGraph, TUNGraph, and TNEANet:

import snap

Graph = snap.GenRndGnm(snap.TNGraph, 100, 1000)
Nodes, Edges = Graph.GetBetweennessCentr(1.0)
for node in Nodes:
    print("node: %d centrality: %f" % (node, Nodes[node]))
for edge in Edges:
    print("edge: (%d, %d) centrality: %f" % (edge.GetVal1(), edge.GetVal2(), Edges[edge]))

UGraph = snap.GenRndGnm(snap.TUNGraph, 100, 1000)
Nodes, Edges = UGraph.GetBetweennessCentr(1.0)
for node in Nodes:
    print("node: %d centrality: %f" % (node, Nodes[node]))
for edge in Edges:
    print("edge: (%d, %d) centrality: %f" % (edge.GetVal1(), edge.GetVal2(), Edges[edge]))

Network = snap.GenRndGnm(snap.TNEANet, 100, 1000)
Nodes, Edges = Network.GetBetweennessCentr(1.0)
for node in Nodes:
    print("node: %d centrality: %f" % (node, Nodes[node]))
for edge in Edges:
    print("edge: (%d, %d) centrality: %f" % (edge.GetVal1(), edge.GetVal2(), Edges[edge]))