All for one, one for all: Consensus community detection in networks

Given an universe of distinct, low-level communities of a network, we aim at identifying the 'meaningful' and consistent communities in this universe. We address this as the process of obtaining consensual community detections and formalize it as a bi-clustering problem. While most consensus algorithms only take into account pairwise relations and end up analyzing a huge matrix, our proposed characterization of the consensus problem (1) does not drop useful information, and (2) analyzes a much smaller matrix, rendering the problem tractable for large networks. We also propose a new pa-rameterless bi-clustering algorithm, fit for the type of matrices we analyze. The approach has proven successful in a very diverse set of experiments, ranging from unifying the results of multiple community detection algorithms to finding common communities from multi-modal or noisy networks.