  Community detection has become an extremely active area of research in recent
years, with researchers proposing various new metrics and algorithms to address
the problem. Recently, the Weighted Community Clustering (WCC) metric was
proposed as a novel way to judge the quality of a community partitioning based
on the distribution of triangles in the graph, and was demonstrated to yield
superior results over other commonly used metrics like modularity. The same
authors later presented a parallel algorithm for optimizing WCC on large
graphs. In this paper, we propose a new distributed, vertex-centric algorithm
for community detection using the WCC metric. Results are presented that
demonstrate the algorithm's performance and scalability on up to 32 worker
machines and real graphs of up to 1.8 billion vertices. The algorithm scales
best with the largest graphs, and to our knowledge, it is the first distributed
algorithm for optimizing the WCC metric.
