User Identity Linkage Across Social Networks by Heterogeneous Graph Attention Network Modeling
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Graph Neural Networks · Complex Network Analysis Techniques · Topic Modeling
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
National Engineering Laboratory for Disaster Backup and Recovery, Information Security Center, School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China
Guizhou Provincial Key Laboratory of Public Big Data, Guizhou University, Guizhou 550025, China
Funding
National Key R&D Program of China · Major Scientific and Technological Special Project of Guizhou Province · Foundation of Guizhou Provincial Key Laboratory of Public Big Data · National Statistical Scientific Research Project of China
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