MCDA: A Parameterless Algorithm for Detecting Communities in Multidimensional Networks

Abstract

This paper introduces a parameterless approach named MCDA: Multidimensional Communities Detection Algorithm. MCDA adopts a local search mechanism which is inspired from the label propagation principle. To this end, we design a novel propagation rule that exploits the most frequently used interaction dimensions among neighbors as an additional constraint for membership selections. The new propagation rule allows MCDA to automatically unfold the hidden communities in a multidimensional context. The detected communities are further processed for relevant dimensions selection using an inter-class inertia-based procedure. The proposed algorithm is fully automated and does not require any parameter to be set by the user to recover communities and their associated dimensions.