Abstract
Cliques are important building blocks for community structure in networks representing structural association between entities. Bicliques play a similar role for bipartite networks representing functional attributes (aka. labels) of entities. We recently proposed a combination of these structures known as labeled-cliques and designed an algorithm to identify them. In this work we show how to use these structures to identify structural-functional communities in networks. We also designed a few metrics to analyse those communities.
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Bera, D. (2021). Maximal Labeled-Cliques for Structural-Functional Communities. In: Benito, R.M., Cherifi, C., Cherifi, H., Moro, E., Rocha, L.M., Sales-Pardo, M. (eds) Complex Networks & Their Applications IX. COMPLEX NETWORKS 2020 2020. Studies in Computational Intelligence, vol 943. Springer, Cham. https://doi.org/10.1007/978-3-030-65347-7_10
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