networkx.algorithms.centrality.edge_betweenness_centrality¶

edge_betweenness_centrality
(G, k=None, normalized=True, weight=None, seed=None)[source]¶ Compute betweenness centrality for edges.
Betweenness centrality of an edge \(e\) is the sum of the fraction of allpairs shortest paths that pass through \(e\)
\[c_B(e) =\sum_{s,t \in V} \frac{\sigma(s, te)}{\sigma(s, t)}\]where \(V\) is the set of nodes, \(\sigma(s, t)\) is the number of shortest \((s, t)\)paths, and \(\sigma(s, te)\) is the number of those paths passing through edge \(e\) [2].
Parameters:  G (graph) – A NetworkX graph.
 k (int, optional (default=None)) – If k is not None use k node samples to estimate betweenness. The value of k <= n where n is the number of nodes in the graph. Higher values give better approximation.
 normalized (bool, optional) – If True the betweenness values are normalized by \(2/(n(n1))\) for graphs, and \(1/(n(n1))\) for directed graphs where \(n\) is the number of nodes in G.
 weight (None or string, optional (default=None)) – If None, all edge weights are considered equal. Otherwise holds the name of the edge attribute used as weight.
 seed (integer, random_state, or None (default)) – Indicator of random number generation state. See Randomness. Note that this is only used if k is not None.
Returns: edges – Dictionary of edges with betweenness centrality as the value.
Return type: dictionary
See also
betweenness_centrality()
,edge_load()
Notes
The algorithm is from Ulrik Brandes [1].
For weighted graphs the edge weights must be greater than zero. Zero edge weights can produce an infinite number of equal length paths between pairs of nodes.
References
[1] A Faster Algorithm for Betweenness Centrality. Ulrik Brandes, Journal of Mathematical Sociology 25(2):163177, 2001. http://www.inf.unikonstanz.de/algo/publications/bfabc01.pdf [2] Ulrik Brandes: On Variants of ShortestPath Betweenness Centrality and their Generic Computation. Social Networks 30(2):136145, 2008. http://www.inf.unikonstanz.de/algo/publications/bvspbc08.pdf