This paper presents a novel consensus community detection (CCD) performed adopting a modularity-based community detection algorithm that exploits the concept of consensus over N independent trials to generate robust communities and to aggregate marginal nodes into a single community. The algorithm is tested on a class of artificial networks with built-in community structure that can be made to reflect the properties of real-world networks. Preliminary results show that CCD outperforms a single run of the original algorithm in terms of Normalised Mutual Information (NMI), number of communities and community size distribution, and provides an effective tool for community detection in real-world networks and a way to overcome the dependence on random seed of modularity-based algorithms.
