| subgraph_centrality {igraph} | R Documentation |
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 should be undirected, but the implementation does not check this currently. |
diag |
Boolean scalar, whether to include the diagonal of the adjacency
matrix in the analysis. Giving |
Details
The subgraph centrality of a vertex is defined as the number of closed loops originating at the vertex, where longer loops are exponentially downweighted.
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 csardi.gabor@gmail.com 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(),
betweenness(),
closeness(),
diversity(),
eigen_centrality(),
harmonic_centrality(),
hub_score(),
page_rank(),
power_centrality(),
spectrum(),
strength()
Examples
g <- sample_pa(100, m = 4, dir = FALSE)
sc <- subgraph_centrality(g)
cor(degree(g), sc)