Analytic Drift Resister for Non-Exemplar Continual Graph Learning

• URL: http://arxiv.org/abs/2604.02633v1
• Date: Fri, 03 Apr 2026 01:58:11 GMT
• Title: Analytic Drift Resister for Non-Exemplar Continual Graph Learning
• Abstract summary: Non-Exemplar Continual Graph Learning (NECGL) seeks to eliminate the privacy risks intrinsic to rehearsal-based paradigms.<n>We propose Analytic Drift Resister (ADR), a novel and theoretically grounded NECGL framework.<n> ADR exploits iterative backpropagation to break free from the frozen pre-trained constraint.
• Score: 23.86709351309769
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Non-Exemplar Continual Graph Learning (NECGL) seeks to eliminate the privacy risks intrinsic to rehearsal-based paradigms by retaining solely class-level prototype representations rather than raw graph examples for mitigating catastrophic forgetting. However, this design choice inevitably precipitates feature drift. As a nascent alternative, Analytic Continual Learning (ACL) capitalizes on the intrinsic generalization properties of frozen pre-trained models to bolster continual learning performance. Nonetheless, a key drawback resides in the pronounced attenuation of model plasticity. To surmount these challenges, we propose Analytic Drift Resister (ADR), a novel and theoretically grounded NECGL framework. ADR exploits iterative backpropagation to break free from the frozen pre-trained constraint, adapting to evolving task graph distributions and fortifying model plasticity. Since parameter updates trigger feature drift, we further propose Hierarchical Analytic Merging (HAM), performing layer-wise merging of linear transformations in Graph Neural Networks (GNNs) via ridge regression, thereby ensuring absolute resistance to feature drift. On this basis, Analytic Classifier Reconstruction (ACR) enables theoretically zero-forgetting class-incremental learning. Empirical evaluation on four node classification benchmarks demonstrates that ADR maintains strong competitiveness against existing state-of-the-art methods.

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