Temporal Topic-Based Multi-Dimensional Social Influence Evaluation in Online Social Networks

Abstract

Analytical applications in online social networks can be generalized as the influence evaluation problem, which targets at finding most influential users. Nowadays social influence evaluation is still an open and challenging issue. Most influence evaluation models focus on the single dimensional evaluation factor but fail to research on the multi-dimensional factors. In this paper, we propose a novel influence evaluation model: the temporal topic influence (TTI) evaluation model, which is a time-aware, content-aware and structure-aware evaluation model. For the aim of multi-dimensional evaluation, we incorporate multi-dimensional measure factors into our model, including the time factor, the topological information and the topic distribution information, etc. We propose a novel concept of user gravitational ability which is inspired by Newton's law of universal gravitation. It can integrate multi-dimensional factors in an appropriate way. Our experiments are conducted on the Sina Weibo data set. Through the experimental analysis, we prove TTI model can calculate users' influences effectively and efficiently. The TTI model can distinguish the value of users' influences. And the TTI model identifies the top-k influential users with higher quality. We also validate the effect of time and topic measure factors in the influence evaluation process.