Supervised Link Prediction Using Multiple Sources

Link prediction is a fundamental problem in social network analysis and modern-day commercial applications such as Facebook and Myspace. Most existing research approaches this problem by exploring the topological structure of a social network using only one source of information in an unsupervised and heuristic manner. However, in many application domains, in addition to the social network of interest, there are a number of auxiliary social networks and/or derived proximity networks available. In this paper we propose a general framework of supervised link prediction from multiple heterogeneous sources. The contribution of the paper is twofold: (1) a supervised learning framework that can effectively and efficiently learn the dynamics of social networks in the presence of auxiliary networks; (2) a feature design scheme for constructing a rich variety of path-based features using multiple sources, and an effective feature selection strategy based on structured sparsity. Extensive exper...

• Type: Journal
• Year: 2010
• Where: ICDM
• Authors: Zhengdong Lu, Berkant Savas, Wei Tang, Inderjit S. Dhillon
• Keywords: Auxiliary Social Networks, Data Mining, ICDM 2010, Multiple Sources, Social Networks