Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning

• URL: http://arxiv.org/abs/2505.10040v1
• Date: Thu, 15 May 2025 07:35:27 GMT
• Title: Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning
• Authors: Lei Song, Jiaxing Li, Shihan Guan, Youyong Kong,
• Abstract summary: Graph Neural Networks endure catastrophic forgetting, undermining their capacity to preserve previously acquired knowledge.<n>We propose Instance-Prototype Affinity Learning (IPAL), a novel paradigm for Non-Exemplar Continual Graph Learning (NECGL)<n>We embed a Decision Boundary Perception mechanism within PCL, fostering greater inter-class discriminability.
• Score: 7.821213342456415
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph Neural Networks (GNN) endure catastrophic forgetting, undermining their capacity to preserve previously acquired knowledge amid the assimilation of novel information. Rehearsal-based techniques revisit historical examples, adopted as a principal strategy to alleviate this phenomenon. However, memory explosion and privacy infringements impose significant constraints on their utility. Non-Exemplar methods circumvent the prior issues through Prototype Replay (PR), yet feature drift presents new challenges. In this paper, our empirical findings reveal that Prototype Contrastive Learning (PCL) exhibits less pronounced drift than conventional PR. Drawing upon PCL, we propose Instance-Prototype Affinity Learning (IPAL), a novel paradigm for Non-Exemplar Continual Graph Learning (NECGL). Exploiting graph structural information, we formulate Topology-Integrated Gaussian Prototypes (TIGP), guiding feature distributions towards high-impact nodes to augment the model's capacity for assimilating new knowledge. Instance-Prototype Affinity Distillation (IPAD) safeguards task memory by regularizing discontinuities in class relationships. Moreover, we embed a Decision Boundary Perception (DBP) mechanism within PCL, fostering greater inter-class discriminability. Evaluations on four node classification benchmark datasets demonstrate that our method outperforms existing state-of-the-art methods, achieving a better trade-off between plasticity and stability.

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