Continual graph learning (CGL) is an important and challenging task that aims to extend static GNNs to dynamic task flow scenarios. As one of the mainstream CGL methods, the experience replay (ER) method receives widespread attention due to its superior performance. However, existing ER methods focus on identifying samples by feature significance or topological relevance, which limits their utilization of comprehensive graph data. In addition, the topology-based ER methods only consider local topological information and add neighboring nodes to the buffer, which ignores the global topological information and increases memory overhead. To bridge these gaps, we propose a novel method called Feature-Topology Fusion-based Experience Replay (FTF-ER) to effectively mitigate the catastrophic forgetting issue with enhanced efficiency. Specifically, from an overall perspective to maximize the utilization of the entire graph data, we propose a highly complementary approach including both feature and global topological information, which can significantly improve the effectiveness of the sampled nodes. Moreover, to further utilize global topological information, we propose Hodge Potential Score (HPS) as a novel module to calculate the topological importance of nodes. HPS derives a global node ranking via Hodge decomposition on graphs, providing more accurate global topological information compared to neighbor sampling. By excluding neighbor sampling, HPS significantly reduces buffer storage costs for acquiring topological information and simultaneously decreases training time. Compared with state-of-the-art methods, FTF-ER achieves a significant improvement of 3.6% in AA and 7.1% in AF on the OGB-Arxiv dataset, demonstrating its superior performance in the class-incremental learning setting.

翻译：持续图学习（CGL）是一项重要且具有挑战性的任务，其目标是将静态图神经网络（GNNs）扩展到动态任务流场景中。作为主流的CGL方法之一，经验回放（ER）方法因其优越的性能而受到广泛关注。然而，现有的ER方法侧重于通过特征重要性或拓扑相关性来识别样本，这限制了对图数据的全面利用。此外，基于拓扑的ER方法仅考虑局部拓扑信息并将相邻节点添加到缓冲区中，这忽略了全局拓扑信息并增加了内存开销。为了弥补这些差距，我们提出了一种名为基于特征-拓扑融合的经验回放（FTF-ER）的新方法，以更高的效率有效缓解灾难性遗忘问题。具体而言，我们从整体视角出发，旨在最大化利用整个图数据，提出了一种高度互补的方法，同时包含特征和全局拓扑信息，这能显著提高采样节点的有效性。此外，为了进一步利用全局拓扑信息，我们提出了霍奇势能分数（HPS）作为一个新模块来计算节点的拓扑重要性。HPS通过对图进行霍奇分解来推导全局节点排序，与邻居采样相比，提供了更准确的全局拓扑信息。通过排除邻居采样，HPS显著降低了获取拓扑信息所需的缓冲区存储成本，同时减少了训练时间。与最先进的方法相比，FTF-ER在OGB-Arxiv数据集上实现了平均准确率（AA）3.6%和遗忘度量（AF）7.1%的显著提升，证明了其在类增量学习设置下的优越性能。