[2604.02633] Analytic Drift Resister for Non-Exemplar Continual Graph Learning

Computer Science > Machine Learning arXiv:2604.02633 (cs) [Submitted on 3 Apr 2026] Title:Analytic Drift Resister for Non-Exemplar Continual Graph Learning Authors:Lei Song, Shihan Guan, Youyong Kong View a PDF of the paper titled Analytic Drift Resister for Non-Exemplar Continual Graph Learning, by Lei Song and 2 other authors View PDF HTML (experimental) 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 re...