UNC.modularity.louvain.und: Louvain Modularity Algorithm on Undirected Graph
In ncullen93/bctR: Brain Connectivity Toolbox in R

Description Usage Arguments Details Value

The optimal community structure is a subdivision of the network into nonoverlapping groups of nodes in a way that maximizes the number of within-group edges, and minimizes the number of between-group edges. The modularity is a statistic that quantifies the degree to which the network may be subdivided into such clearly delineated groups.

1	UNC.modularity.louvain.und(W, gamma = 1, hierarchy = FALSE, seed = NA)

`W`	: a Matrix - undirected weighted/binary connection matrix
`gamma`	: a float - resolution parameter. default value=1. Values 0 <= gamma < 1 detect larger modules while gamma > 1 detects smaller modules.
`hierarchy`	: a boolean - enables hier. output
`seed`	: an integer - random seed

The Louvain algorithm is a fast and accurate community detection algorithm (as of writing). The algorithm may also be used to detect hierarchical community structure.

R Microbenchmark - Fast enough.. Unit: milliseconds expr min lq mean median uq max neval fun 8.890078 11.65477 12.90705 12.62741 13.85725 19.57911 100

WITH compile::cmpfun() - Fast! Unit: milliseconds expr min lq mean median uq max neval fun 6.015344 7.543102 9.385713 9.529057 10.69335 13.49019 100

Note: Function is not validated yet.

ciQ : a list - two elements where element one is 'ci', a vector (refined community affiliation network), and element two is 'Q', a float (optimized modularity metric).If hierarchical output enabled, becomes an Hx1 array of floats instead.

ncullen93/bctR documentation built on May 23, 2019, 1:28 p.m.