User Identification across Social Networks Based on Global View Features

Nowadays, people prefer to take part in multiple social networks to enjoy different kinds of services. Consequently, a significant task is to identify users across networks. Most state-of-the-art works on this issue exploit user local structure features (e.g., friend, follow and followed). In this paper, we first proposes the notion of user global view features, which represent the location of users in the network. Then, we present an iterative two-stage algorithm (GAUI) using Global view features with user Attribute features to solve User Identification. In GAUI, we iteratively update pairwise similarity and predict new matching users. Certainly, we present a community based core anchor link filter strategy to reduce the computation cost, and present a stable matching based mapping strategy to improve the accuracy. At last, the experiments conducted on two real-world aligned networks demonstrate that our method has better performance on precision and recall.