btwn_centr_mult: Calculate betweenness centrality for a list of graphs.

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

Description

This function is a convenience wrapper for igraph's betweenness centrality function and takes as an input a list of igraph objects.

Usage

btwn_centr_mult(
  graphs,
  col.names = NULL,
  row.names = NULL,
  parallel = FALSE,
  cores = NA
)

Arguments

graphs	A list of igraph objects.
col.names	The names of each column (node labels).
row.names	The names of each row (subject).
parallel	Should multiple cores be used? Defaults to FALSE. If TRUE, progress bar is not displayed. This is normal.
cores	How many cores should be used? Defaults to recommended 1 less than number of CPU cores.

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 matrix of the betweenness centralities of each node for each subject.

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

Examples

betweenness = btwn_centr_mult(graphs,row.names = subj_numbers,col.names = node_labels)

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