A Novel Cross-Network Embedding for Anchor Link Prediction with Social Adversarial Attacks

Abstract

Anchor link prediction across social networks plays an important role in multiple social network analysis. Traditional methods rely heavily on user privacy information or high-quality network topology information. These methods are not suitable for multiple social networks analysis in real-life. Deep learning methods based on graph embedding are restricted by the impact of the active privacy protection policy of users on the graph structure. In this paper, we propose a novel method which neutralizes the impact of users’ evasion strategies. First, graph embedding with conditional estimation analysis is used to obtain a robust embedding vector space. Secondly, cross-network features space for supervised learning is constructed via the constraints of cross-network feature collisions. The combination of robustness enhancement and cross-network feature collisions constraints eliminate the impact of evasion strategies. Extensive experiments on large-scale real-life social networks demonstrate that the proposed method significantly outperforms the state-of-the-art methods in terms of precision, adaptability, and robustness for the scenarios with evasion strategies.