Social Influencer Analysis with Factorization Machines

Abstract

How will the reputations of individuals in a social network be influenced by their communities in a quantitative way? This work attempts to observe the collaborative events occurring at individuals involved in a social network to obtain such crucial knowledge. We propose a Factorization Machine approach to find out the latent social influence among the individuals based on their collaborations. Experiments conducted on a real-world DBLP dataset verify that the proposed approach can discover the latent social influence among individuals and provide a better predictive model than several baselines.