btwn_centr: Calculate betweenness 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 betweenness centrality function for consistency with rsfcNet's naming conventions.

Usage

btwn_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

Betweenness centrality gives the number of times a given node lies in the shortest path between two other nodes. Betweenness centrality may be less natural to interpret in rsfc networks (see Power et al 2013) but remains a popular centrality metric. See for example Wang, Zuo, & He (2010). It is calculated with the formula below:

b_i = ∑_{s \neq v \neq t}\frac{σ_{st}(i)}{σ_{st}}

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

Power, J. D., Schlaggar, B. L., Lessov-Schlaggar, C. N., & Petersen, S. E. (2013). Evidence for hubs in human functional brain networks. Neuron, 79(4), 10.1016/j.neuron.2013.07.035. http://doi.org/10.1016/j.neuron.2013.07.035

Wang, J., Zuo, X., & He, Y. (2010). Graph-Based Network Analysis of Resting-State Functional MRI. Frontiers in Systems Neuroscience, 4, 16. http://doi.org/10.3389/fnsys.2010.00016

See Also

btwn_centr_mult

Examples

betweenness = btwn_centr(graph)

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