Incremental learning is a machine learning approach that involves training a model on a sequence of tasks, rather than all tasks at once. This ability to learn incrementally from a stream of tasks is crucial for many real-world applications. However, incremental learning is a challenging problem on graph-structured data, as many graph-related problems involve prediction tasks for each individual node, known as Node-wise Graph Incremental Learning (NGIL). This introduces non-independent and non-identically distributed characteristics in the sample data generation process, making it difficult to maintain the performance of the model as new tasks are added. In this paper, we focus on the inductive NGIL problem, which accounts for the evolution of graph structure (structural shift) induced by emerging tasks. We provide a formal formulation and analysis of the problem, and propose a novel regularization-based technique called Structural-Shift-Risk-Mitigation (SSRM) to mitigate the impact of the structural shift on catastrophic forgetting of the inductive NGIL problem. We show that the structural shift can lead to a shift in the input distribution for the existing tasks, and further lead to an increased risk of catastrophic forgetting. Through comprehensive empirical studies with several benchmark datasets, we demonstrate that our proposed method, Structural-Shift-Risk-Mitigation (SSRM), is flexible and easy to adapt to improve the performance of state-of-the-art GNN incremental learning frameworks in the inductive setting.

翻译：增量学习是一种机器学习方法，涉及按任务序列而非一次性训练所有任务。这种从任务流中增量学习的能力对于许多实际应用至关重要。然而，在图结构数据上进行增量学习极具挑战性，因为许多图相关问题涉及对每个节点单独进行预测，即节点级图增量学习（NGIL）。这导致样本数据生成过程中具有非独立同分布特性，使得随着新任务加入而保持模型性能变得困难。本文聚焦于归纳式NGIL问题，该问题考虑了由新任务引发的图结构演化（结构偏移）。我们对该问题进行了形式化定义与分析，并提出了一种名为结构偏移风险缓解（SSRM）的新型正则化技术，以减轻结构偏移对归纳式NGIL问题灾难性遗忘的影响。研究表明，结构偏移会导致已有任务的输入分布发生偏移，进而增加灾难性遗忘的风险。通过在多个基准数据集上的全面实证研究，我们证明所提出的结构偏移风险缓解（SSRM）方法灵活且易于适配，能够提升现有最先进GNN增量学习框架在归纳设定下的性能。