Community detection in social networks: the power of ensemble methods

In this work, we present an original seed-centric algorithm for community detection. Instead of expanding communities around selected seeds as most of existing seed-centric approaches do, we propose applying an ensemble clustering approach to different network partitions derived from local communities computed for each seed. Local communities are themselves computed applying an ensemble ranking approach that allow combining different local modularity functions that are used in a classical greedy optimization process.