UNC.modularity.louvain.und.sign: Louvain Algorithm on Undirected Signed Graphs
In ncullen93/bctR: Brain Connectivity Toolbox in R

Description Usage Arguments Details Value

The optimal community structure is a subdivision of the network into non-overlapping 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 clearly delineated groups.

1	UNC.modularity.louvain.und.sign(W, gamma = 1, qtype = "sta", seed = NA)

`W`	: a Matrix - undirected weighted/binary connection matrix with positive and negative weights
`gamma`	: a float - resolution parameter, default value = 1. Values 0 <= gamma < 1 detect larger modules while gamme > 1 detects smaller modules
`qtype`	: a string - can be 'sta' (default), 'pos', 'smp', 'gja', 'neg'. See Rubinov and Sporns (2011) for a description.
`seed`	: an integer - the random seed

The Louvain algorithm is a fast and accurate community detection algorithm.

Use this function as opposed to the modularity.louvain.und() only if hte network contains a mix of positive and negative weights. If the network contains all positive weights, the output of the two functions will be equivalent.

Note: Function is not validated/running 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)

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