linkcomm provides tools for the generation, visualization, and analysis of link communities in networks of arbitrary size and type.
Link communities reveal the nested and overlapping structure in networks, and uncover the key nodes that form connections to multiple communities.
linkcomm provides tools for generating, visualizing, and analysing link communities in networks of arbitrary size and type.
For a more detailed overview of how to use the package:
vignette(topic = "linkcomm", package = "linkcomm")
To run an interactive demonstration of
linkcomm within R:
demo(topic = "linkcomm", package = "linkcomm")
Alex T. Kalinka email@example.com
Ahn, Y.Y., Bagrow, J.P., and Lehmann, S. (2010). Link communities reveal multiscale complexity in networks. Nature 466, 761-764.
Becker, E., Robisson, B., Chapple, C.E., Guenoche, A. and Brun, C. (2012) Multifunctional proteins revealed by overlapping clustering in protein interaction network. Bioinformatics 28, 84-90.
Kalinka, A.T. and Tomancak, P. (2011). linkcomm: an R package for the generation, visualization, and analysis of link communities in networks of arbitrary size and type. Bioinformatics 27, 2011-2012.
Spencer, R. (2010). http://scaledinnovation.com/analytics/communities/comlinks.html
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## Generate graph and extract link communities. g <- swiss[,3:4] lc <- getLinkCommunities(g) ## Plot a graph layout of the link communities. plot(lc, type = "graph") ## Use a Spencer circle layout. plot(lc, type = "graph", layout = "spencer.circle") ## Calculate a community-based measure of node centrality. getCommunityCentrality(lc) ## Find nested communities. getAllNestedComm(lc) ## Uncover the relatedness between communities. getClusterRelatedness(lc)
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