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Continual learning on graph data has recently attracted paramount attention for its aim to resolve the catastrophic forgetting problem on existing tasks while adapting the sequentially updated model to newly emerged graph tasks.
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2022
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L.-P. Xhonneux, M. Qu, and J. Tang, “Continuous graph neural networks,” in
2020
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Y. Luo, Z. Huang, Z. Zhang, Z. Wang, M. Baktashmotlagh, and Y. Yang, “Learning from the past: continual meta-learning with bayesian graph neural networks,” in
2020
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B. Tang and D. S. Matteson, “Graph-based continual learning,”
2020
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J. Wang, G. Song, Y. Wu, and L. Wang, “Streaming graph neural networks via continual learning,” in
2020
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2020
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Y. Ma, Z. Guo, Z. Ren, J. Tang, and D. Yin, “Streaming graph neural networks,” in
2020
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Y. Feng, J. Jiang, and Y. Gao, “Incremental learning on growing graphs,” 2020
2020
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S. Kim, S. Yun, and J. Kang, “Dygrain: An incremental learning framework for dynamic graphs,” in
2022
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C. Wang, Y. Qiu, D. Gao, and S. Scherer, “Lifelong graph learning,” in
2022
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2022
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X. Zhang, D. Song, and D. Tao, “Hierarchical prototype networks for continual graph representation learning,”
2023
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L. Galke, I. Vagliano, B. Franke, T. Zielke, M. Hoffmann, and A. Scherp, “Lifelong learning on evolving graphs under the constraints of imbalanced classes and new classes,”
2023
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Q. Shen, W. Ren, and W. Qin, “Graph relation aware continual learning,”
2023
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2023
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2023
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F. G. Febrinanto, F. Xia, K. Moore, C. Thapa, and C. Aggarwal, “Graph lifelong learning: A survey,”
2023
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Y. Ren, L. Ke, D. Li, H. Xue, Z. Li, and S. Zhou, “Incremental graph classification by class prototype construction and augmentation,” in
2023
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2023
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S. Gupta, S. Manchanda, S. Ranu, and S. J. Bedathur, “Grafenne: learning on graphs with heterogeneous and dynamic feature sets,” in
2023
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2023
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B. Wang, Y. Zhang, X. Wang, P. Wang, Z. Zhou, L. Bai, and Y. Wang, “Pattern expansion and consolidation on evolving graphs for continual traffic prediction,” in
2023
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J. Omeliyanenko, A. Zehe, A. Hotho, and D. Schlör, “Capskg: Enabling continual knowledge integration in language models for automatic knowledge graph completion,” in
2023
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M. Masana, X. Liu, B. Twardowski, M. Menta, A. D. Bagdanov, and J. van de Weijer, “Class-incremental learning: Survey and performance evaluation on image classification,”
2023
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2023
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2023
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2023
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2023
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L. Hedegaard, N. Heidari, and A. Iosifidis, “Continual spatio-temporal graph convolutional networks,”
2023
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A. Zaman, F. Yangyu, M. S. Ayub, M. Irfan, L. Guoyun, and L. Shiya, “Cmdgat: Knowledge extraction and retention based continual graph attention network for point cloud registration,”
2023
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2023
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2023
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F. G. Febrinanto, F. Xia, K. Moore, C. Thapa, and C. Aggarwal, “Graph lifelong learning: A survey,”
2023
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T. D. Hoang, D. V. Tung, D.-H. Nguyen, B.-S. Nguyen, H. H. Nguyen, and H. Le, “Universal graph continual learning,”
2023
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2023
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2023
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L. Sun, J. Ye, H. Peng, F. Wang, and S. Y. Philip, “Self-supervised continual graph learning in adaptive riemannian spaces,” in
2023
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2023
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2023
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J. Su and C. Wu, “Towards robust inductive graph incremental learning via experience replay,”
2023
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J. Su, D. Zou, Z. Zhang, and C. Wu, “Towards robust graph incremental learning on evolving graphs,” in
2023
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2023
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2023
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2024
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H. Lin, R. Jia, and X. Lyu, “Gated attention with asymmetric regularization for transformer-based continual graph learning,” in
2025
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