User identity linkage across social networks via linked heterogeneous network embedding
- Publication Type:
- Journal Article
- World Wide Web, 2019, 22 (6), pp. 2611 - 2632
- Issue Date:
|Wang2018_Article_UserIdentityLinkageAcrossSocia.pdf||Published Version||2.93 MB|
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© 2018, Springer Science+Business Media, LLC, part of Springer Nature. User identity linkage has important implications in many cross-network applications, such as user profile modeling, recommendation and link prediction across social networks. To discover accurate cross-network user correspondences, it is a critical prerequisite to find effective user representations. While structural and content information describe users from different perspectives, there is a correlation between the two aspects of information. For example, a user who follows a celebrity tends to post content about the celebrity as well. Therefore, the projections of structural and content information of a user should be as close to each other as possible, which inspires us to fuse the two aspects of information in a unified space. However, owing to the information heterogeneity, most existing methods extract features from content and structural information respectively, instead of describing them in a unified way. In this paper, we propose a Linked Heterogeneous Network Embedding model (LHNE) to learn the comprehensive representations of users by collectively leveraging structural and content information in a unified framework. We first model the topics of user interests from content information to filter out noise. Next, cross-network structural and content information are embedded into a unified space by jointly capturing the friend-based and interest-based user co-occurrence in intra-network and inter-network, respectively. Meanwhile, LHNE learns user transfer and topic transfer for enhancing information exchange across networks. Empirical results show LHNE outperforms the state-of-the-art methods on both real social network and synthetic datasets and can work well even with little or no structural information.
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