networkx.algorithms.centrality.subgraph_centrality¶

subgraph_centrality(G)[source]¶

Returns subgraph centrality for each node in G.

Subgraph centrality of a node n is the sum of weighted closed walks of all lengths starting and ending at node n. The weights decrease with path length. Each closed walk is associated with a connected subgraph (1).

Parameters: G (graph)
Returns: nodes – Dictionary of nodes with subgraph centrality as the value.
Return type: dictionary
Raises: NetworkXError – If the graph is not undirected and simple.

See also

subgraph_centrality_exp(): Alternative algorithm of the subgraph centrality for each node of G.

Notes

This version of the algorithm computes eigenvalues and eigenvectors of the adjacency matrix.

Subgraph centrality of a node u in G can be found using a spectral decomposition of the adjacency matrix 1,

\[SC(u)=\sum_{j=1}^{N}(v_{j}^{u})^2 e^{\lambda_{j}},\]

where v_j is an eigenvector of the adjacency matrix A of G corresponding corresponding to the eigenvalue lambda_j.

Examples

(Example from 1) >>> G = nx.Graph([(1,2),(1,5),(1,8),(2,3),(2,8),(3,4),(3,6),(4,5),(4,7),(5,6),(6,7),(7,8)]) >>> sc = nx.subgraph_centrality(G) >>> print([‘%s %0.2f’%(node,sc[node]) for node in sorted(sc)]) [‘1 3.90’, ‘2 3.90’, ‘3 3.64’, ‘4 3.71’, ‘5 3.64’, ‘6 3.71’, ‘7 3.64’, ‘8 3.90’]

References

1(1,2,3): Ernesto Estrada, Juan A. Rodriguez-Velazquez, “Subgraph centrality in complex networks”, Physical Review E 71, 056103 (2005). https://arxiv.org/abs/cond-mat/0504730