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Community detection has become an extremely active area of research in recentyears, with researchers proposing various new metrics and algorithms to addressthe problem. Recently, the Weighted Community Clustering (WCC) metric wasproposed as a novel way to judge the quality of a community partitioning basedon the distribution of triangles in the graph, and was demonstrated to yieldsuperior results over other commonly used metrics like modularity. The sameauthors later presented a parallel algorithm for optimizing WCC on largegraphs. In this paper, we propose a new distributed, vertex-centric algorithmfor community detection using the WCC metric. Results are presented thatdemonstrate the algorithm's performance and scalability on up to 32 workermachines and real graphs of up to 1.8 billion vertices. The algorithm scalesbest with the largest graphs, and to our knowledge, it is the first distributedalgorithm for optimizing the WCC metric.

Distributed Community Detection with the WCC Metric