Anchor link prediction using topological information in social networks

Abstract

People today may participate in multiple social networks (Facebook, Twitter, Google+, etc.). Predicting the correspondence of the accounts that refer to the same natural person across multiple social networks is a significant and challenging problem. Formally, social networks that outline the relationships of a common group of people are defined as aligned networks, and the correspondence of the accounts that refer to the same natural person across aligned networks are defined as anchor links. In this paper, we learn the problem of Anchor Link Prediction (ALP). Firstly, two similarity metrics (Bi-Similarity BiS and Reliability Similarity ReS) are proposed to measure the similarity between nodes in aligned networks. And we prove mathematically that the node pair with the maximum BiS has higher probability to be an anchor link and a correctly predicted anchor link must have high ReS. Secondly, we present an iterative algorithm to solve the problem of ALP efficiently. Also, we discuss the termination of the algorithm to give a tradeoff between precision and recall. Finally, we conduct a series of experiments on both synthetic social networks and real social networks to confirm the effectiveness of our approach.