betweenness_centrality

betweenness_centrality(G, nodes)

Compute betweenness centrality for nodes in a bipartite network.

Betweenness centrality of a node v is the sum of the fraction of all-pairs shortest paths that pass through v.

Values of betweenness are normalized by the maximum possible value which for bipartite graphs is limited by the relative size of the two node sets [1].

Let n be the number of nodes in the node set U and m be the number of nodes in the node set V, then nodes in U are normalized by dividing by

\[\frac{1}{2} [m^2 (s + 1)^2 + m (s + 1)(2t - s - 1) - t (2s - t + 3)] ,\]

where

\[s = (n - 1) \div m , t = (n - 1) \mod m ,\]

and nodes in V are normalized by dividing by

\[\frac{1}{2} [n^2 (p + 1)^2 + n (p + 1)(2r - p - 1) - r (2p - r + 3)] ,\]

where,

\[p = (m - 1) \div n , r = (m - 1) \mod n .\]

Parameters:

Ggraph

A bipartite graph

nodeslist or container

Container with all nodes in one bipartite node set.

Returns:

betweennessdictionary

Dictionary keyed by node with bipartite betweenness centrality as the value.

See also

degree_centrality

closeness_centrality

sets()

is_bipartite()

Notes

The nodes input parameter must contain all nodes in one bipartite node set, but the dictionary returned contains all nodes from both node sets. See bipartite documentation for further details on how bipartite graphs are handled in NetworkX.

References

[1]

Borgatti, S.P. and Halgin, D. In press. “Analyzing Affiliation Networks”. In Carrington, P. and Scott, J. (eds) The Sage Handbook of Social Network Analysis. Sage Publications. https://dx.doi.org/10.4135/9781446294413.n28

Examples

>>> G = nx.cycle_graph(4)
>>> top_nodes = {1, 2}
>>> nx.bipartite.betweenness_centrality(G, nodes=top_nodes)
{0: 0.25, 1: 0.25, 2: 0.25, 3: 0.25}
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Additional backends implement this function

cugraph : GPU-accelerated backend.