DyGRAIN: An Incremental Learning Framework for Dynamic Graphs

Seoyoon Kim, Seongjun Yun, Jaewoo Kang

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

2 Citations (Scopus)


Graph-structured data provide a powerful representation of complex relations or interactions. Many variants of graph neural networks (GNNs) have emerged to learn graph-structured data where underlying graphs are static, although graphs in various real-world applications are dynamic (e.g., evolving structure). To consider the dynamic nature that a graph changes over time, the need for applying incremental learning (i.e., continual learning or lifelong learning) to the graph domain has been emphasized. However, unlike incremental learning on Euclidean data, graph-structured data contains dependency between the existing nodes and newly appeared nodes, resulting in the phenomenon that receptive fields of existing nodes vary by new inputs (e.g., nodes and edges). In this paper, we raise a crucial challenge of incremental learning for dynamic graphs as time-varying receptive fields, and propose a novel incremental learning framework, DyGRAIN, to mitigate time-varying receptive fields and catastrophic forgetting. Specifically, our proposed method incrementally learns dynamic graph representations by reflecting the influential change in receptive fields of existing nodes and maintaining previous knowledge of informative nodes prone to be forgotten. Our experiments on large-scale graph datasets demonstrate that our proposed method improves the performance by effectively capturing pivotal nodes and preventing catastrophic forgetting.

Original languageEnglish
Title of host publicationProceedings of the 31st International Joint Conference on Artificial Intelligence, IJCAI 2022
EditorsLuc De Raedt, Luc De Raedt
PublisherInternational Joint Conferences on Artificial Intelligence
Number of pages7
ISBN (Electronic)9781956792003
Publication statusPublished - 2022
Event31st International Joint Conference on Artificial Intelligence, IJCAI 2022 - Vienna, Austria
Duration: 2022 Jul 232022 Jul 29

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
ISSN (Print)1045-0823


Conference31st International Joint Conference on Artificial Intelligence, IJCAI 2022

Bibliographical note

Funding Information:
This work was funded by National Research Foundation of Korea (NRF-2020R1A2C3010638), Ministry of Health Welfare, Republic of Korea (HR20C0021), and ICT Creative Consilience program(IITP-2021-0-01819).

Publisher Copyright:
© 2022 International Joint Conferences on Artificial Intelligence. All rights reserved.

ASJC Scopus subject areas

  • Artificial Intelligence


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