neighbor_centr_mult: Calculate neighbor centrality for multiple graphs

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

Description

This calculates the average strength or degree of each node's connections for multiple networks.

Usage

neighbor_centr_mult(graphs, col.names = NULL, row.names = NULL)

Arguments

col.names	The names of each column (node labels).
row.names	The names of each row (subject).
graph	A list of networks in matrix format or as an igraph object. Can be weighted (and signed) or binary.

Details

This is an alternative to igraph's knn function. The name was changed from knn to avoid confusion with functions of the same name relating to the machine learning method k-nearest neighbors. The neighbor centrality of node is the mean strength or degree of all of its neighbors (connections).

Value

A matrix of the average strength/degree of a node's neighbors for each graph.

Author(s)

Brandon Vaughan

References

Barrat, A., Barthelemy, M., Pastor-Satorras, R., Vespignani, A. (2004). The architecture of complex weighted networks, Proc. Natl. Acad. Sci. USA 101, 3747

Fornito, A., Zalesky, A., & Bullmore, E. (2016). Node Degree and Strength. Chapter 4. Fundamentals of Brain Network Analysis, 115-136. doi:10.1016/B978-0-12-407908-3.00004-2

Rubinov, M., & Sporns, O. (2011). Weight-conserving characterization of complex functional brain networks. NeuroImage, 56(4), 2068-2079. doi:10.1016/j.neuroimage.2011.03.069

See Also

strength_multiple degree_centr neighbor_centr knn

Examples

neighbor_centr_mult(graphs)

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