User Preference Based Link Inference for Social Network

Yuqing Sun, Haoran Xu, Elisa Bertino, Demin Li

Abstract: In this paper, we focus on the link predication problem in social networks. Our approach is based on the observation that there is a large amount of social behavior taking place every day which contains substantial information about user intrinsic characteristics that influence the dynamics of social networks. In order to obtain a deeper understanding of user behavior, we introduce the concept of latent factor to capture the motivation behind social activities. Since user relationships are often asymmetric, we also take into account bilateral user wishes with respect to friend as preferences, which is beyond traditional approaches or overall measurements. Two combination modes are proposed, independent fusion and interdependent fusion, to integrate these hybrid metrics with traditional measurements for link inference. In order to quantify the sensitivity of each element in metrics we use information theory. Experimental results on several real datasets show that our approach has better performance than previous methods.