AL-GNN: 基于分析学习的隐私保护且无需重放的持续图学习
AL-GNN: Privacy-Preserving and Replay-Free Continual Graph Learning via Analytic Learning
AI总结：
提出AL-GNN框架，利用分析学习理论将持续图学习转化为递归最小二乘优化，通过闭式分类器更新和正则化特征自相关矩阵实现无需反向传播和重放缓冲的高效训练，在保护隐私的同时提升性能并减少遗忘。
摘要：
持续图学习（CGL）旨在使图神经网络能够从图结构数据流中增量学习，而不会遗忘先前获得的知识。现有方法，特别是基于经验重放的方法，通常存储并重新访问过去的图数据以缓解灾难性遗忘。然而，这些方法存在显著局限性，包括隐私问题和低效性。在这项工作中，我们提出了AL-GNN，一种新颖的持续图学习框架，消除了对反向传播和重放缓冲区的需求。相反，AL-GNN利用分析学习理论的原理，将学习形式化为递归最小二乘优化过程。它通过闭式分类器更新和正则化特征自相关矩阵来分析和更新模型知识。这种设计使得每个任务能够进行高效的单次训练，并通过避免存储历史样本固有地保护数据隐私。在多个动态图分类基准上的大量实验表明，AL-GNN取得了与现有方法相比具有竞争力或更优的性能。例如，它在CoraFull上平均性能提高了10%，在Reddit上遗忘减少了30%以上，同时由于其无反向传播的设计，训练时间减少了近50%。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
