SAOT: Self-Supervised Continual Graph Learning with Structure-Aware Optimal Transport
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Computer Science > Machine Learning
arXiv:2607.00377 (cs)
[Submitted on 1 Jul 2026]
Title:SAOT: Self-Supervised Continual Graph Learning with Structure-Aware Optimal Transport
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Abstract:Self-supervised Continual Graph Learning (CGL) aims to successively learn from a graph sequence with different tasks without label supervision - a paradigm that has attracted widespread attention. Most existing self-supervised CGL methods rely on instance-level consistency objectives that enforce stability of individual node (or node-pair) embeddings. Due to optimizing nodes in isolation, these methods fail to maintain global relational structure, causing inter-node correspondences to progressively distort under continual learning. To this end, we propose a novel Structure-Aware Optimal Transport (SAOT) framework that explicitly captures and preserves relational structure within graph representations across sequential tasks. Specifically, SAOT leverages optimal transport theory to capture global inter-node correspondences, thereby facilitating and enhancing graph representation learning. Simultaneously, SAOT incorporates a cross-task knowledge distillation mechanism to preserve the previous structural knowledge. Extensive experiments on four CGL benchmark datasets demonstrate that SAOT outperforms existing self-supervised baselines. In particular, SAOT achieves significant performance gains, improving average accuracy by up to 5% on CoraFull-CL and over 15% on Products-CL compared with state-of-the-art methods in the Class-IL setting.
| Comments: | The paper has 9 pages of text and 13 pages in total (including acknowledgments, impact statement, references, and appendix), with 6 figures and 4 tables. This paper has been accepted by ICML 2026 conference and this is a final version of the manuscript submitted to the conference |
| Subjects: | Machine Learning (cs.LG); Social and Information Networks (cs.SI) |
| Cite as: | arXiv:2607.00377 [cs.LG] |
| (or arXiv:2607.00377v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2607.00377
arXiv-issued DOI via DataCite (pending registration)
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