Abstract
Today, social networks are becoming increasingly popular and indispensable, where users usually have multiple accounts. It is of considerable significance to conduct user identity linkage across social networks. We can comprehensively depict diversified characteristics of user behaviors, accurately model user profiles, conduct recommendations across social networks, and track cross social network user behaviors by user identity linkage. Existing works mainly focus on a specific type of user profile, user-generated content, and structural information. They have problems of weak data expression ability and ignored potential relationships, resulting in unsatisfactory performances of user identity linkage. Recently, graph neural networks have achieved excellent results in graph embedding, graph representation, and graph classification. As a graph has strong relationship expression ability, we propose a user identity linkage method based on a heterogeneous graph attention network mechanism (UIL-HGAN). Firstly, we represent user profiles, user-generated content, structural information, and their features in a heterogeneous graph. Secondly, we use multiple attention layers to aggregate user information. Finally, we use a multi-layer perceptron to predict user identity linkage. We conduct experiments on two real-world datasets: OSCHINA-Gitee and Facebook-Twitter. The results validate the effectiveness and advancement of UIL-HGAN by comparing different feature combinations and methods.
Highlights
With the rapid development of the Internet and information technology, social networks are becoming more and more indispensable and various
We propose a user identity linkage method based on a heterogeneous graph attention network mechanism (UIL-HGAN)
We propose a novel method to represent user profiles, user-generated content, structural information, and their features in a heterogeneous graph; We propose a novel user identity linkage method based on a heterogeneous graph attention network mechanism called UIL-HGAN; We conduct experiments on two real-world datasets to test and validate the effectiveness and advancement of UIL-HGAN
Summary
With the rapid development of the Internet and information technology, social networks are becoming more and more indispensable and various. We can use GNN to learn feature representation and aggregate user profiles, user-generated content, and structural information in the latent space. To solve problems of weak data expression ability and ignored potential relationships, firstly, user profiles, user-generated content, structural information, and their features are represented as nodes in a heterogeneous graph from different social networks, respectively. We use multiple attention layers to represent users by aggregating user profiles, user-generated content, and structural information in the latent space. We propose a novel method to represent user profiles, user-generated content, structural information, and their features in a heterogeneous graph; We propose a novel user identity linkage method based on a heterogeneous graph attention network mechanism called UIL-HGAN; We conduct experiments on two real-world datasets to test and validate the effectiveness and advancement of UIL-HGAN
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