用随机固定编码器生成稳定节点表示，显著缓解在线图学习中的灾难性遗忘。
The Unreasonable Effectiveness of Randomized Representations in Online Continual Graph Learning
- 固定随机初始化编码器，仅在线训练轻量分类器。
- 在多个基准上提升达30%，接近离线联合训练上限。
- 适合追求简单高效、无需记忆缓冲的在线图学习场景。
灾难性遗忘是在线持续图学习（OCGL）的主要障碍，其中节点按序到达，分布漂移可能随时发生，且无法对特定任务子图进行离线训练。本文探索了一种出人意料的简单但高效的方法：使用固定且随机初始化的编码器，通过聚合邻域信息生成鲁棒而丰富的节点嵌入，仅在线训练轻量级分类器。冻结编码器可消除表示参数的漂移，这是遗忘的关键来源，从而获得既丰富又稳定的嵌入。在多个OCGL基准上评估显示，尽管方法简单且无记忆缓冲，该方法仍持续优于现有最先进方法，性能提升最高达30%，常接近离线联合训练的理论上限。结果表明，在OCGL中，通过拥抱架构简洁性和稳定性，可无需复杂重放或正则化即有效最小化灾难性遗忘。
原文摘要 · Abstract (English)
Catastrophic forgetting is one of the main obstacles for Online Continual Graph Learning (OCGL), where nodes arrive one by one, distribution drifts may occur at any time and offline training on task-specific subgraphs is not feasible. In this work, we explore a surprisingly simple yet highly effective approach for OCGL: we use a fixed, randomly initialized encoder to generate robust and expressive node embeddings by aggregating neighborhood information, training online only a lightweight classifier. By freezing the encoder, we eliminate drifts of the representation parameters, a key source of forgetting, obtaining embeddings that are both expressive and stable. When evaluated across several OCGL benchmarks, despite its simplicity and lack of memory buffer, this approach yields consistent gains over state-of-the-art methods, with surprising improvements of up to 30% and performance often approaching that of the joint offline-training upper bound. These results suggest that in OCGL, catastrophic forgetting can be minimized without complex replay or regularization by embracing architectural simplicity and stability.
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