subgraph_centrality: Find subgraph centrality scores of network positions

In igraph: Network Analysis and Visualization

Find subgraph centrality scores of network positions

Description

Subgraph centrality of a vertex measures the number of subgraphs a vertex participates in, weighting them according to their size.

Usage

subgraph_centrality(graph, diag = FALSE)

Arguments

graph	The input graph. It will be treated as undirected.
diag	Boolean scalar, whether to include the diagonal of the adjacency matrix in the analysis. Giving FALSE here effectively eliminates the loops edges from the graph before the calculation.

Details

The subgraph centrality of a vertex is defined as the number of closed walks originating at the vertex, where longer walks are downweighted by the factorial of their length.

Currently the calculation is performed by explicitly calculating all eigenvalues and eigenvectors of the adjacency matrix of the graph. This effectively means that the measure can only be calculated for small graphs.

Value

A numeric vector, the subgraph centrality scores of the vertices.

Author(s)

Gabor Csardi [e-mail] based on the Matlab code by Ernesto Estrada

References

Ernesto Estrada, Juan A. Rodriguez-Velazquez: Subgraph centrality in Complex Networks. Physical Review E 71, 056103 (2005).

See Also

eigen_centrality(), page_rank()

Centrality measures alpha_centrality(), authority_score(), betweenness(), closeness(), diversity(), eigen_centrality(), harmonic_centrality(), hits_scores(), page_rank(), power_centrality(), spectrum(), strength()

Examples

g <- sample_pa(100, m = 4, dir = FALSE)
sc <- subgraph_centrality(g)
cor(degree(g), sc)

igraph documentation built on Oct. 20, 2024, 1:06 a.m.