Modeling for user interaction by influence transfer effect in online social networks

Abstract

User interaction is one of the most important features of online social networks, and is the basis of research of user behavior analysis, information spreading model, etc. However, existing approaches focus on the interactions between adjacent nodes, which do not fully take the interactions and relationship between local region users into consideration as well as the details of interaction process. In this paper, we find that there exists influence transfer effect in the process of user interactions, and present a regional user interaction model to analyze and understand interactions between users in a local region by influence transfer effect. Based on real data from Sina Weibo, we validate the effectiveness of our model by the experiments of user type classification, influential user identification and zombie user identification in online social networks. The experimental results show that our model present better performance than the PageRank based method and machine learning method.