Streaming Graph Neural Networks

Yao Ma, Ziyi Guo, Zhaocun Ren, Jiliang Tang, Dawei Yin

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

126 Scopus citations

Abstract

Graphs are used to model pairwise relations between entities in many real-world scenarios such as social networks. Graph Neural Networks(GNNs) have shown their superior ability in learning representations for graph structured data, which leads to performance improvements in many graph related tasks such as link prediction, node classification and graph classification. Most of the existing graph neural networks models are designed for static graphs while many real-world graphs are inherently dynamic with new nodes and edges constantly emerging. Existing graph neural network models cannot utilize the dynamic information, which has been shown to enhance the performance of many graph analytic tasks such as community detection. Hence, in this paper, we propose DyGNN, a Dynamic Graph Neural Network model, which can model the dynamic information as the graph evolving. In particular, the proposed framework keeps updating node information by capturing the sequential information of edges (interactions), the time intervals between edges and information propagation coherently. Experimental results on various dynamic graphs demonstrate the effectiveness of the proposed framework.

Original languageEnglish (US)
Title of host publicationSIGIR 2020 - Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval
PublisherAssociation for Computing Machinery, Inc
Pages719-728
Number of pages10
ISBN (Electronic)9781450380164
DOIs
StatePublished - Jul 25 2020
Externally publishedYes
Event43rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2020 - Virtual, Online, China
Duration: Jul 25 2020Jul 30 2020

Publication series

NameSIGIR 2020 - Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval

Conference

Conference43rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2020
Country/TerritoryChina
CityVirtual, Online
Period7/25/207/30/20

All Science Journal Classification (ASJC) codes

  • Computer Graphics and Computer-Aided Design
  • Information Systems
  • Software

Keywords

  • dynamic graphs
  • graph neural networks

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