An efficient multilevel scheme for coarsening large scale social networks

The explosive growth of data raised from social networks, hinders researchers from analysing them in a good way. So, is it possible to rapidly “zoom-out” from this huge network while preserving its whole structure? In fact, this technique is named “graph’s reduction” and represents a significant task in social networks’ analysis. Thus, several methods have been developed to pull a smaller succinct version of the graph. Some of them belong to the category of “graph sampling” and risk losing key characteristics of communities. Others are part of “coarsening strategy” and designed to cope with the problem of community discovering, which is our desired purpose. In this paper, we propose a multi-level coarsening algorithm called MCCA (Multi-level Coarsening Compact Areas). The main strategy of this algorithm is to merge well connected zones in every level by updating edge and vertex weight until a stopping criterion is met. Using real-world social networks, we evaluate the quality and scalability of MCCA. Furthermore, we compared it with eight known proposals. We also show how our method can be used as a preliminary step for community detection without much loss of information.