Multivariate relations aggregation learning in social networks

Jin Xu, Shuo Yu, Ke Sun, Jing Ren, Ivan Lee, Shirui Pan, Feng Xia

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


Multivariate relations are general in various types of networks, such as biological networks, social networks, transportation networks, and academic networks. Due to the principle of ternary closures
and the trend of group formation, the multivariate relationships in social networks are complex and rich. Therefore, in graph learning tasks of social networks, the identification and utilization of
multivariate relationship information are more important. Existing graph learning methods are based on the neighborhood information diffusion mechanism, which often leads to partial omission or even lack of multivariate relationship information, and ultimately affects the accuracy and execution efficiency of the task. To address these challenges, this paper proposes the multivariate relationship aggregation learning (MORE) method, which can effectively capture the multivariate relationship information in the network environment. By aggregating node attribute features and structural features, MORE achieves higher accuracy and faster convergence speed. We conducted experiments on one citation network and five social networks. The experimental results show that the MORE model has higher accuracy than the GCN (Graph Convolutional
Network) model in node classification tasks, and can significantly reduce time cost.
Original languageEnglish
Title of host publicationProceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020
EditorsDaqing He, Sally Jo Cunningham, Preben Hansen
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Number of pages10
ISBN (Electronic)9781450375856
Publication statusPublished - Aug 2020
EventACM Conference on Digital Libraries 2020 - Virtual Event, China
Duration: 1 Aug 20205 Aug 2020 (Proceedings) (Website)


ConferenceACM Conference on Digital Libraries 2020
Abbreviated titleJCDL ’20
Internet address


  • Multivariate Relations
  • Network Motif
  • Graph Learning
  • Network Science

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