Consensus community detection for multi-dimensional networks


Since their beginnings, social networks have affected the way people communicate and interact with each other. Nowadays, user interactions range from social relations to posting and reading activities, leading to the existence of multiple and complementary information sources or dimensions for characterising user behaviour. The task of community detection could benefit from integrating those multiple sources. However, most techniques disregard the effect of information aggregation, and continue to focus only on one aspect: network topology. This paper aims at providing some insights on how to integrate the multiple and heterogeneous social media information sources characterising user activities and behaviour to optimise the quality of found communities. To that end, diverse consensus strategies to extend techniques designed for a unique information source to multi-dimensional networks are presented and analysed. Experimental evaluation confirmed the benefits of using consensus strategies for leveraging on multiple data dimensions in terms of community quality.