A uniform framework for community detection via influence maximization in social networks

Community structure as a significant feature helps us understand networks in a mesoscopic view. Existing approaches for community detection haven't considered about the formation of communities, whereas community in real social networks is usually established around influential nodes. In this paper, we present an efficient and effective framework based on local influence to detect both overlapping and hierarchical communities. We try to illuminate two fundamental questions: 1)Whether local influence regarded as a new property can affect the formation of communities; 2)How to quantify node's local influence and utilize it to detect communities. To demonstrate the effectiveness of local influence in terms of evaluating node importance, nodes with high local influence are selected to perform the influence maximization experiments on real social networks. Experimental results show that our framework is effective and efficient for both community detection and influence maximization.