Compressive Sensing over Graphs Based Inter-Community Detection Scheme in Mobile Social Networks

Recently, mobile social networks (MSNs) has been playing an increasingly large proportion in people's daily life, and consequently attracted enormous amount of researches in this area, including network data collection, user behavior analysis and so on. Many of them are based on the community structure of the MSNs, the detection of which has attracted academic attention. In this paper, we propose an inter-community detection scheme under the framework of the emerging compressive sensing (CS) over graphs. Firstly, we extract the social structure among users by calculating the probability of two users encountering with each other. Then, the proposed scheme utilizes the encounter probability and the edge-clustering coefficient to define a novel additive property to make the CS algorithm applicable. Simulation results demonstrate that the proposed detection scheme can detect inter- community links more accurately than the conventional random walk based method.