An Accurate Probabilistic Model for Community Evolution Analysis in Social Network

Abstract

The scope of the work is to build a framework able to study the evolution of a set of communities based on their underlying social activities. Generally, for a given community, many subgroups may exist and evolve with different and various opinions or behaviors. So, in this paper, we will focus on the identification of the potential subgroups and their potential relation/correlation corresponding to self-similarity over time. Clearly, we want to know if the subgroups remain unchanged then being stable or might they evolve to merge by forming new groups. In this respect, social engagement that refers to the participation of actors from a community to the activities of a social group is used to distribute activities into several classes. So building subgroups will be our first challenge and analyzing temporal correlation between them will be another interesting issue in this present work. The first problem can be solved by analyzing the activities inside the given initial community. We believe that, in many situations, activities should be characterized by parametric distributions as the gaussians. So, by means of the gaussian mixture modeler (GMM), subgroups can be identified successfully. Thereafter, the intrinsic relation between subgroups and their temporal evolution can be studied clearly with the calibration of hidden Markov models (HMM). The achievement of this study can help management operators to take decisions in two ways: i) since each GMM subgroup may correspond to a single individual's opinion/behavior, typical decision could be made for a given social group ii) also, the manager can take advantageous decisions by merging opinions for subgroups which have self-similarities, the HMM is here to learn more about this issue. We show the effectiveness of our approach by using real life data from Reddit.com.