Individual Analysis Model and Related Research in Social Networks

As the rapid development of mobile Internet, social networking platforms make it possible for people to create, communicate and share information with each other whenever and wherever they are. The mode and effect of information propagation within social networks can be directly determined by users and their interactions. Individual analysis, as the key part of social network analysis, helps to understand the sophisticated mechanism of information dissemination from the perspective of users. Existing work all analyzes individual users primarily based on their behaviors or characteristics embedded in social networking data. In this paper, we have summarized a novel individual analysis model to unify tag recommendation, influence measurement and sentiment analysis with regard to the same user, which is convenient for social scientists to further study. We also introduce some classic and modified algorithms derived from the proposed models respectively, and illustrate how these algorithms map to models. Furthermore, our unified model can offer a reference and point out the optimization directions for individual analysis in social networks.