分析漂移电阻器用于非示例连续图学习
Analytic Drift Resister for Non-Exemplar Continual Graph Learning
AI总结：
本文提出分析漂移电阻器（ADR），通过迭代反向传播和分层分析合并（HAM）解决非示例连续图学习中的特征漂移问题，实现理论零遗忘的类增量学习。
摘要：
非示例连续图学习（NECGL）旨在通过保留类级原型表示而非原始图示例来消除基于回放的范式的隐私风险，以缓解灾难性遗忘。然而，这种设计选择不可避免地导致特征漂移。作为新兴替代方案，分析持续学习（ACL）利用冻结预训练模型的内在泛化特性来增强持续学习性能。然而，其关键缺点在于模型可塑性的显著减弱。为克服这些挑战，我们提出分析漂移电阻器（ADR），一种新颖且理论严谨的NECGL框架。ADR利用迭代反向传播突破冻结预训练约束，适应演化的任务图分布并强化模型可塑性。由于参数更新会触发特征漂移，我们进一步提出分层分析合并（HAM），通过岭回归在图神经网络（GNNs）中逐层合并线性变换，从而确保绝对抗特征漂移。在此基础上，分析分类重构（ACR）实现了理论零遗忘的类增量学习。在四个节点分类基准上的实证评估表明，ADR在现有最先进方法中保持了强大的竞争力。
英文摘要：
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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