Probabilistic Community Detection in Social Networks

The detection of community structures is a very crucial research area. The problem of community detection has received considerable attention from a large portion of the scientific community. More importantly, these articles are spread across a large number of different disciplines, from computer science, to statistics, and social sciences. The analysis of modern social networks becomes rather cumbersome, as their size and number keeps growing larger and larger. Moreover, in the modern communities, users participate in large number of groups. From the network perspective, efficient methods should be developed to automatically identify overlapping communities, that is, communities with overlapping nodes. In this work, we use a probabilistic network model to characterize and identify linked communities with common nodes. The innovative idea in this work is that the communities are represented as Markovian networks with continuously changing states. Each state represents the number of users within a cluster, that have specific characteristic classes. Based on the current state, we introduce a fast, linear on the number of newly added users, approach to estimate the probability of each cluster to be homogeneous in terms of sets of user characteristics and to determine how well the new user fit within a community. Because of the linear computations involved, our proposed probabilistic model can detect communities and overlaps with low execution time and high accuracy, as shown in our experimental results. The experimental results have shown that our probabilistic scheme executes faster and provides more robust communities compared to competitive schemes.