Relation Prediction via Graph Neural Network in Heterogeneous Information Networks with Missing Type Information

Han Zhang, Yu Hao, Xin Cao, Yixiang Fang, Won Yong Shin, Wei Wang

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

3 Citations (Scopus)

Abstract

Relation prediction is a fundamental task in network analysis which aims to predict the relationship between two nodes. Thus, this differes from the traditional link prediction problem predicting whether a link exists between a pair of nodes, which can be viewed as a binary classification task. However, in the heterogeneous information network (HIN) which contains multiple types of nodes and multiple relations between nodes, the relation prediction task is more challenging. In addition, the HIN might have missing relation types on some edges and missing node types on some nodes, which makes the problem even harder. In this work, we propose RPGNN, a novel relation prediction model based on the graph neural network (GNN) and multi-task learning to solve this problem. Existing GNN models for HIN representation learning usually focus on the node classification/clustering task. They require the type information of all edges and nodes and always learn a weight matrix for each type, thus requiring a large number of learning parameters on HINs with rich schema. In contrast, our model directly encodes and learns relations in HIN and avoids the requirement of type information during message passing in GNN. Hence, our model is more robust to the missing types for the relation prediction task on HINs. The experiments on real HINs show that our model can consistently achieve better performance than several state-of-the-art HIN representation learning methods.

Original languageEnglish
Title of host publicationCIKM 2021 - Proceedings of the 30th ACM International Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Pages2517-2526
Number of pages10
ISBN (Electronic)9781450384469
DOIs
Publication statusPublished - 2021 Oct 26
Event30th ACM International Conference on Information and Knowledge Management, CIKM 2021 - Virtual, Online, Australia
Duration: 2021 Nov 12021 Nov 5

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Conference

Conference30th ACM International Conference on Information and Knowledge Management, CIKM 2021
Country/TerritoryAustralia
CityVirtual, Online
Period21/11/121/11/5

Bibliographical note

Publisher Copyright:
© 2021 ACM.

All Science Journal Classification (ASJC) codes

  • General Business,Management and Accounting
  • General Decision Sciences

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