SAOT: Self-Supervised Continual Graph Learning with Structure-Aware Optimal Transport

• URL: http://arxiv.org/abs/2607.00377v1
• Date: Wed, 01 Jul 2026 03:21:23 GMT
• Title: SAOT: Self-Supervised Continual Graph Learning with Structure-Aware Optimal Transport
• Abstract summary: Self-supervised Continual Graph Learning (CGL) aims to successively learn from a graph sequence with different tasks without label supervision.<n>Most existing self-supervised methods rely on instance-level consistency objectives that enforce stability of individual node (or node-pair) embeddings.
• Score: 35.822409009079955
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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