Description Usage Arguments Details Value Author(s) References See Also Examples
This function is a convenience wrapper for igraph's betweenness centrality function and takes as an input a list of igraph objects.
1 2 3 4 5 6 7 |
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. |
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}}
A matrix of the betweenness centralities of each node for each subject.
Brandon Vaughan
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
1 | betweenness = btwn_centr_mult(graphs,row.names = subj_numbers,col.names = node_labels)
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