Social Influence Analysis Based On Modeling Interactions In Dynamic Social Networks: A Case Study

Interactions occur across social networks, and modeling interactions in dynamic social networks is a challenging research problem that has broad applications. By combining topology in mathematics with field theory in physics, topology potential, which sets up a virtual field via a topology space to reflect individual activities, local effects and preferential attachments in different interactions, has been proposed to model mutual effects between individuals on social networks. In this paper, we take into consideration not only the information of topology structure and content but also two factors, namely, individual mass and interaction strength. From the perspective of smooth evolution of social networks, we propose a method based on dynamic topology potential, which captures the correlations between different changing snapshots of a social network and can be used to model interactions dynamically, so as to quantify the effects of interactions between individuals on dynamic social networks. Finally, we utilize the dynamic topology potential method for user influence analysis, especially for influential user identification, and the experiment conducted on a real-world data set from AMiner demonstrates the feasibility and effectiveness of our method in terms of a measure for network robustness.