edge_betweenness_centrality_subset#
- edge_betweenness_centrality_subset(G, sources, targets, normalized=False, weight=None)[source]#
Compute betweenness centrality for edges for a subset of nodes.
\[c_B(e) = \sum_{s \in S, t \in T} \frac{\sigma(s, t | e)}{\sigma(s, t)}\]where \(S\) is the set of sources, \(T\) is the set of targets, \(\sigma(s, t)\) is the number of shortest \((s, t)\)-paths, and \(\sigma(s, t | e)\) is the number of those paths passing through edge \(e\) [1]. The denominator \(\sigma(s, t)\) is a normalization factor that can be turned off to get the raw path counts.
- Parameters:
- Ggraph
A networkx graph.
- sources: list of nodes
Nodes to use as sources for shortest paths in betweenness.
- targets: list of nodes
Nodes to use as targets for shortest paths in betweenness.
- normalizedbool, optional (default=False)
If
True, the betweenness values are rescaled by dividing by the number of possible \((s, t)\)-pairs in the graph.- weightNone or string, optional (default=None)
If
None, all edge weights are 1. Otherwise holds the name of the edge attribute used as weight. Weights are used to calculate weighted shortest paths, so they are interpreted as distances.
- Returns:
- edgesdict
Dictionary of edges with betweenness centrality as the value.
See also
Notes
The basic algorithm is from [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.
The normalization might seem a little strange but it is the same as in edge_betweenness_centrality() and is designed to make edge_betweenness_centrality(G) be the same as edge_betweenness_centrality_subset(G,sources=G.nodes(),targets=G.nodes()).
References
[1] (1,2)Ulrik Brandes: On Variants of Shortest-Path Betweenness Centrality and their Generic Computation. Social Networks 30(2):136-145, 2008. https://doi.org/10.1016/j.socnet.2007.11.001