Institute of Information Science, Academia Sinica

Social influence has been a significant and popular topic in the social network analysis. People usually rely on the social influence model to predict and learn the influence diffusion process in real world. We studied the most popular influence models nowadays and found out some limitations of the traditional influence models. Traditional models only categorize nodes into two types of states, say active and inactive states. In addition, most previous models have only taken positive influences into account. We try to break the above limitations and propose a novel social influence model based on multiple states and negative social influence. According to the new propagation method, the strength of the social influence may be reduced over time. The experimental results show that the proposed model outperforms previous models in precisions of prediction.