rSpectral: Spectral Modularity Clustering
Implements the network clustering algorithm described in
Newman (2006) <doi:10.1103/PhysRevE.74.036104>. The complete
iterative algorithm comprises of two steps. In the first step, the
network is expressed in terms of its leading eigenvalue and
eigenvector and recursively partition into two communities.
Partitioning occurs if the maximum positive
eigenvalue is greater than the tolerance (10e-5) for the current
partition, and if it results in a positive contribution to the
Given an initial separation using the leading eigen step, 'rSpectral'
then continues to maximise for the change in Modularity using a
fine-tuning step - or variate thereof. The first stage here is to
find the node which, when moved from one community to another,
gives the maximum change in Modularity. This node’s community is
then fixed and we repeat the process until all nodes have been moved.
The whole process is repeated from this new state until the change
in the Modularity, between the new and old state, is less than the
A slight variant of the fine-tuning step, which can improve speed
of the calculation, is also provided. Instead of moving each node
into each community in turn, we only consider moves of neighbouring
nodes, found in different communities, to the community of the
current node of interest. The two steps process is repeatedly
applied to each new community found, subdivided each community
into two new communities, until we are unable to find any division
that results in a positive change in Modularity.
||R (≥ 3.5.0)
||Rcpp (≥ 126.96.36.199), Rdpack, igraph, graph
||Rcpp, RcppArmadillo (≥ 0.11.2.0.0)
||RColorBrewer, Rgraphviz, igraphdata, testthat (≥ 3.0.0)
||Colin Mclean [aut] (algorithm implementation in Rcpp functions),
Anatoly Sorokin [aut, cre] (R functions, cranification, documentation,
||Anatoly Sorokin <lptolik at gmail.com>
||rSpectral citation info
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