AL-GNN无需存储历史数据，实现高效隐私保护的图学习。
AL-GNN: Privacy-Preserving and Replay-Free Continual Graph Learning via Analytic Learning
- 用递归最小二乘法解析更新模型，避免反向传播和重放缓冲区
- 在CoraFull上性能提升10%，Reddit上遗忘率降低30%
- 适合注重隐私与训练效率的动态图学习场景
持续图学习（CGL）旨在使图神经网络能够从流式图数据中增量学习，同时不遗忘先前知识。现有方法多依赖经验重放，需存储并回放历史图数据以缓解灾难性遗忘，但存在隐私泄露和效率低下的问题。本文提出AL-GNN框架，摒弃反向传播与重放缓冲区，基于解析学习理论将学习过程建模为递归最小二乘优化，通过闭式分类器更新与正则化特征自相关矩阵，实现每任务一次遍历的高效训练，并天然保护数据隐私。在多个动态图分类基准上的实验表明，AL-GNN表现优于或相当现有方法：在CoraFull上平均性能提升10%，在Reddit上遗忘率降低超30%，且因无反向传播设计，训练时间减少近50%。
原文摘要 · Abstract (English)
Continual graph learning (CGL) aims to enable graph neural networks to incrementally learn from a stream of graph structured data without forgetting previously acquired knowledge. Existing methods particularly those based on experience replay typically store and revisit past graph data to mitigate catastrophic forgetting. However, these approaches pose significant limitations, including privacy concerns, inefficiency. In this work, we propose AL GNN, a novel framework for continual graph learning that eliminates the need for backpropagation and replay buffers. Instead, AL GNN leverages principles from analytic learning theory to formulate learning as a recursive least squares optimization process. It maintains and updates model knowledge analytically through closed form classifier updates and a regularized feature autocorrelation matrix. This design enables efficient one pass training for each task, and inherently preserves data privacy by avoiding historical sample storage. Extensive experiments on multiple dynamic graph classification benchmarks demonstrate that AL GNN achieves competitive or superior performance compared to existing methods. For instance, it improves average performance by 10% on CoraFull and reduces forgetting by over 30% on Reddit, while also reducing training time by nearly 50% due to its backpropagation free design.
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