# networkx.algorithms.centrality.communicability_betweenness_centrality¶

communicability_betweenness_centrality(G, normalized=True)[source]

Returns subgraph communicability for all pairs of nodes in G.

Communicability betweenness measure makes use of the number of walks connecting every pair of nodes as the basis of a betweenness centrality measure.

Parameters

G (graph)

Returns

nodes – Dictionary of nodes with communicability betweenness as the value.

Return type

dictionary

Raises

NetworkXError – If the graph is not undirected and simple.

Notes

Let G=(V,E) be a simple undirected graph with n nodes and m edges, and A denote the adjacency matrix of G.

Let G(r)=(V,E(r)) be the graph resulting from removing all edges connected to node r but not the node itself.

The adjacency matrix for G(r) is A+E(r), where E(r) has nonzeros only in row and column r.

The subraph betweenness of a node r is 1

$\omega_{r} = \frac{1}{C}\sum_{p}\sum_{q}\frac{G_{prq}}{G_{pq}}, p\neq q, q\neq r,$

where G_{prq}=(e^{A}_{pq} - (e^{A+E(r)})_{pq} is the number of walks involving node r, G_{pq}=(e^{A})_{pq} is the number of closed walks starting at node p and ending at node q, and C=(n-1)^{2}-(n-1) is a normalization factor equal to the number of terms in the sum.

The resulting omega_{r} takes values between zero and one. The lower bound cannot be attained for a connected graph, and the upper bound is attained in the star graph.

References

1

Ernesto Estrada, Desmond J. Higham, Naomichi Hatano, “Communicability Betweenness in Complex Networks” Physica A 388 (2009) 764-774. https://arxiv.org/abs/0905.4102

Examples

>>> G = nx.Graph(
...     [
...         (0, 1),
...         (1, 2),
...         (1, 5),
...         (5, 4),
...         (2, 4),
...         (2, 3),
...         (4, 3),
...         (3, 6),
...     ]
... )
>>> cbc = nx.communicability_betweenness_centrality(G)
>>> print([f"{node} {cbc[node]:0.2f}" for node in sorted(cbc)])
['0 0.03', '1 0.45', '2 0.51', '3 0.45', '4 0.40', '5 0.19', '6 0.03']