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An evolutionary motifs-based algorithm for community detection

Motifs are considered fundamental building blocks of complex networks. As such, these patterns of interconnections, similarly to the interactions between edges exploited by the traditional community detection algorithms, may give insights on how networks are organized in modules. The aim of this work is to identify clusters of network motifs. We propose an approach based on genetic algorithms for clustering nodes according to their participation in instances of particular motifs. The algorithm finds the best local solution by partitioning the network into a number of communities that minimizes the concept of motif conductance. A comparison with state-of-the-art methods on several real-world networks shows that our genetic approach is able to better capture the community structure of networks.