closeness_centr: Calculate closeness centrality for a single graph.

In abnormally-distributed/rsfcNet: Analyze resting state functional connectivity networks

Description

This function is a convenience wrapper for igraph's closeness function for consistency with rsfcNet's naming conventions.

Usage

closeness_centr(graph, weights = abs(E(graph)$weight))

Arguments

graph	A network as an igraph object or matrix.
weights	a vector of edge weights. Defaults to the absolute value of the input network.

Details

Closeness centrality gives the average shortest path length betwen a node i and all other nodes.

\frac{N-1}{∑_{j\neq i}^N d(n_i,e_{ij})}

Value

A vector of centrality scores for each node.

Author(s)

Brandon Vaughan

References

Fornito, A., Zalesky, A., & Bullmore, E. (2016). Centrality and Hubs. Chapter 5. Fundamentals of Brain Network Analysis, 137-161. doi:10.1016/b978-0-12-407908-3.00005-4

Freeman, L.C. (1979). Centrality in Social Networks I: Conceptual Clarification. Social Networks, 1, 215-239.

See Also

closeness_centr_mult current_centr

Examples

closeness = closeness_centr_mult(graphs,row.names = subj_numbers,col.names = node_labels)

abnormally-distributed/rsfcNet documentation built on March 8, 2020, 5:32 p.m.