Abstract

A core task of social network analysis is to predict the formation of new social links. In the context of social media, link prediction serves as the foundation for forecasting the evolution of the follower graph and predicting interactions and the flow of information between users. Previous link prediction methods have generally represented the social network as a graph and leveraged topological and semantic measures of similarity between two nodes to estimate the probability of a new link between them. In this work, we suggest another link creation mechanism for social media that is based on the ease of discovering the new node. Specifically, a user v creates a link to another user u after seeing u’s name on his or her screen; in other words, visibility of a user (name) is a necessary condition for new link formation. We propose a model for link prediction, which estimates the probability a user will see another user’s name, and use this model to predict new links. We estimate a set of parameters in the proposed model using Maximum-Likelihood and Minorize-Maximize methods. Empirical results show that the proposed model can more accurately predict both follow and co-mention links than alternative state-of-the-art methods. Our work suggests that the effort required to discover a new social contact is negatively correlated with link formation, and the easier it is to discover a user, the higher the likelihood a link will be created.

Date

June 16, 2014

Authors

Linhong Zhu, Kristina Lerman

Publisher

Academy of Science and Engineering (ASE), USA, ASE 2014