Community structure detection in networks based on the leading eigenvector of the community matrix
An adjacency matrix. Should be symmetric with diagonal containing zeros.
The difference between this function and the algorithm described by Newman is that this function can be used on an adjacency matrix with negative elements, which is very common for correlation matrices and other measures of pairwise synchrony of time series.
cluseigen returns a list with one element for each of the splits
performed by the clustering algorithm. Each element is a vector with entries
corresponding to rows and columns of adj and indicating the module membership
of the node, following the split. The last element of the list is the final
clustering determined by the algorithm when its halting condition is satisfied.
The first element is always a vector of all 1s (corresponding to before any
splits are performed).
Gomez S., Jensen P. & Arenas A. (2009). Analysis of community structure in networks of correlated data. Phys Rev E, 80, 016114.
Newman M.E.J. (2006). Finding community structure in networks using the eigenvectors of matrices. Phys Rev E, 74, 036104.
Newman M.E.J. (2006) Modularity and community structure in networks. PNAS 103, 8577-8582.
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