Learning triadic influence in large social networks

Social influence has been a widely accepted phenomenon in social networks for decades. In this paper, we study influence from the perspective of structure, and focus on the simplest group structure - triad. We analyze two different genres of behavior: Retweeting on Weibo and Paying on CrossFire. We have several intriguing observations from these two networks. First, different internal structures of one's friends exhibit significant heterogeneity in influence patterns. Second, the strength of social relationship plays an important role in influencing one's behavior, and more interestingly, it is not necessarily positively correlated with the strength of social influence. We incorporate the triadic influence patterns into a predictive model to predict user's behavior. Experiment results show that our method can significantly improved the prediction accuracy.