Inductive node-wise graph incremental learning is a challenging task due to the dynamic nature of evolving graphs and the dependencies between nodes. In this paper, we propose a novel experience replay framework, called Structure-Evolution-Aware Experience Replay (SEA-ER), that addresses these challenges by leveraging the topological awareness of GNNs and importance reweighting technique. Our framework effectively addresses the data dependency of node prediction problems in evolving graphs, with a theoretical guarantee that supports its effectiveness. Through empirical evaluation, we demonstrate that our proposed framework outperforms the current state-of-the-art GNN experience replay methods on several benchmark datasets, as measured by metrics such as accuracy and forgetting.

翻译：归纳式节点级图增量学习是一项具有挑战性的任务，原因在于演化图的动态性质以及节点间的依赖关系。本文提出了一种新颖的经验回放框架，称为结构演化感知经验回放（SEA-ER），该框架通过利用图神经网络的拓扑感知能力和重要性重加权技术来应对这些挑战。我们的框架有效解决了演化图中节点预测问题的数据依赖性，并提供了理论保证以支持其有效性。通过实证评估，我们证明所提出的框架在多个基准数据集上，就准确率和遗忘率等指标而言，超越了当前最先进的GNN经验回放方法。