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Continual Graph Learning (CGL) enables models to incrementally learn from streaming graph-structured data without forgetting previously acquired knowledge.
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J. Wang, G. Song, Y. Wu, L. Wang, Streaming graph neural networks via continual learning, CIKM ’20, Association for Computing Machinery, New York, NY, USA, 2020, p. 1515–1524
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Y. Liu, R. Qiu, Z. Huang, Cat: Balanced continual graph learning with graph condensation, in: 2023 IEEE International Conference on Data Mining (ICDM), IEEE, 2023, pp. 1157–1162
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P. Zhang, Y. Yan, C. Li, S. Wang, X. Xie, G. Song, S. Kim, Continual learning on dynamic graphs via parameter isolation, in: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’23, Association for Computing Machinery, New York, NY, USA, 2023, p. 601–611 · 2023
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