Relation learning on social networks with multi-modal graph edge variational autoencoders

Carl Yang, Jieyu Zhang, Haonan Wang, Sha Li, Myungwan Kim, Matt Walker, Yiou Xiao, Jiawei Han

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

While node semantics have been extensively explored in social networks, little research attention has been paid to profile edge semantics, i.e., social relations. Ideal edge semantics should not only show that two users are connected, but also why they know each other and what they share in common. However, relations in social networks are often hard to profile, due to noisy multi-modal signals and limited user-generated ground-truth labels. In this work, we aim to develop a unified and principled framework that can profile user relations as edge semantics in social networks by integrating multi-modal signals in the presence of noisy and incomplete data. Our framework is also flexible towards limited or missing supervision. Specifically, we assume a latent distribution of multiple relations underlying each user link, and learn them with multi-modal graph edge variational autoencoders. We encode the network data with a graph convolutional network, and decode arbitrary signals with multiple reconstruction networks. Extensive experiments and case studies on two public DBLP author networks and two internal LinkedIn member networks demonstrate the superior effectiveness and efficiency of our proposed model.

Original languageEnglish (US)
Title of host publicationWSDM 2020 - Proceedings of the 13th International Conference on Web Search and Data Mining
PublisherAssociation for Computing Machinery, Inc
Pages699-707
Number of pages9
ISBN (Electronic)9781450368223
DOIs
StatePublished - Jan 20 2020
Event13th ACM International Conference on Web Search and Data Mining, WSDM 2020 - Houston, United States
Duration: Feb 3 2020Feb 7 2020

Publication series

NameWSDM 2020 - Proceedings of the 13th International Conference on Web Search and Data Mining

Conference

Conference13th ACM International Conference on Web Search and Data Mining, WSDM 2020
CountryUnited States
CityHouston
Period2/3/202/7/20

Keywords

  • Graph variational autoencoder
  • Relation learning
  • Social networks

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Software
  • Computer Science Applications

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  • Cite this

    Yang, C., Zhang, J., Wang, H., Li, S., Kim, M., Walker, M., Xiao, Y., & Han, J. (2020). Relation learning on social networks with multi-modal graph edge variational autoencoders. In WSDM 2020 - Proceedings of the 13th International Conference on Web Search and Data Mining (pp. 699-707). (WSDM 2020 - Proceedings of the 13th International Conference on Web Search and Data Mining). Association for Computing Machinery, Inc. https://doi.org/10.1145/3336191.3371829