Detecting communities in social networks using label propagation with information entropy

Community detection has become an important and effective methodology to understand the structure and function of real world networks. The label propagation algorithm (LPA) is a near-linear time algorithm used to detect non-overlapping community. However, it merely considers the direct neighbor relationship. In this paper, we propose an algorithm to consider information entropy as the measurement of the relationship between direct neighbors and indirect neighbors. In a label update, we proposed a new belonging coefficient to describe the weight of the label. With the belonging coefficient no less than a threshold each node can keep one or more labels to constitute an overlapping community. Experimental results on both real-world and benchmark networks show that our algorithm also possesses high accuracy on detecting community structure in networks.