Online user representation learning across heterogeneous social networks

Weiqing Wang, Hongzhi Yin, Xingzhong Du, Wen Hua, Yongjun Li, Quoc Viet Hung Nguyen

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

6 Citations (Scopus)


Accurate user representation learning has been proven fundamental for many social media applications, including community detection, recommendation, etc. A major challenge lies in that, the available data in a single social network are usually very limited and sparse. In real life, many people are members of several social networks in the same time. Constrained by the features and design of each, any single social platform offers only a partial view of a user from a particular perspective. In this paper, we propose MV-URL, a multiview user representation learning model to enhance user modeling by integrating the knowledge from various networks. Different from the traditional network embedding frameworks where either the whole framework is single-network based or each network involved is a homogeneous network, we focus on multiple social networks and each network in our task is a heterogeneous network. It's very challenging to effectively fuse knowledge in this setting as the fusion depends upon not only the varying relatedness of information sources, but also the target application tasks. MV-URL focuses on two tasks: user account linkage (i.e., to predict the missing true user account linkage across social media) and user attribute prediction. Extensive evaluations have been conducted on two real-world collections of linked social networks, and the experimental results show the superiority of MV-URL compared with existing state-of-art embedding methods. It can be learned online, and is trivially parallelizable. These qualities make it suitable for real world applications.

Original languageEnglish
Title of host publicationProceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval
EditorsYoelle Maarek, Jian-Yun Nie, Falk Scholer
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Number of pages10
ISBN (Electronic)9781450361729
Publication statusPublished - 2019
EventACM International Conference on Research and Development in Information Retrieval 2019 - Paris, France
Duration: 21 Jul 201925 Jul 2019
Conference number: 42nd


ConferenceACM International Conference on Research and Development in Information Retrieval 2019
Abbreviated titleSIGIR 2019
Internet address


  • Heterogeneous networks
  • Multiview
  • Representation learning
  • Social networks
  • User modelling

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