面向动态图的在线持续学习，高效更新模型避免遗忘。
Online Continual Graph Learning
- 在严格资源限制下，逐批处理节点数据流，实现即时推理。
- 提出新基准，包含7个数据集和9种策略，支持标准化评估。
- 设计轻量级基线模型，兼顾性能与计算效率，适合实际部署。
持续学习旨在增量获取新知识的同时缓解灾难性遗忘。在线持续学习（OCL）关注从数据流中单次或小批量观测值出发，快速、增量式地更新模型。将OCL扩展至图结构数据至关重要，因许多现实世界网络随时间演化，需及时进行在线预测。然而，现有持续或流式图学习方法通常假设可访问完整图快照或多轮任务遍历，违背了在线设置的效率约束。为此，我们引入在线持续图学习（OCGL）设定，形式化在严格内存与计算预算下对演化图的节点级持续学习。OCGL定义了模型如何增量处理节点信息流，同时保持任意时刻推理能力并满足资源限制。我们进一步建立了一个涵盖七项数据集与九种持续学习策略的综合性基准，适配于OCGL设定，实现标准化评估。最后，基于基准测试结果，我们提出一种极简但具有竞争力的基线方法，在保证高效率的同时取得优异的实证表现。
原文摘要 · Abstract (English)
Continual Learning (CL) aims to incrementally acquire new knowledge while mitigating catastrophic forgetting. Within this setting, Online Continual Learning (OCL) focuses on updating models promptly and incrementally from single or small batches of observations from a data stream. Extending OCL to graph-structured data is crucial, as many real-world networks evolve over time and require timely, online predictions. However, existing continual or streaming graph learning methods typically assume access to entire graph snapshots or multiple passes over tasks, violating the efficiency constraints of the online setting. To address this gap, we introduce the Online Continual Graph Learning (OCGL) setting, which formalizes node-level continual learning on evolving graphs under strict memory and computational budgets. OCGL defines how a model incrementally processes a stream of node-level information while maintaining anytime inference and respecting resource constraints. We further establish a comprehensive benchmark comprising seven datasets and nine CL strategies, suitably adapted to the OCGL setting, enabling a standardized evaluation setup. Finally, we present a minimalistic yet competitive baseline for OCGL, inspired by our benchmarking results, that achieves strong empirical performance with high efficiency.
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