Graph classification is essential for understanding complex biological systems, where molecular structures and interactions are naturally represented as graphs. Traditional graph neural networks (GNNs) perform well on static tasks but struggle in dynamic settings due to catastrophic forgetting. We present Perturbed and Sparsified Continual Graph Learning (PSCGL), a robust and efficient continual graph learning framework for graph data classification, specifically targeting biological datasets. We introduce a perturbed sampling strategy to identify critical data points that contribute to model learning and a motif-based graph sparsification technique to reduce storage needs while maintaining performance. Additionally, our PSCGL framework inherently defends against graph backdoor attacks, which is crucial for applications in sensitive biological contexts. Extensive experiments on biological datasets demonstrate that PSCGL not only retains knowledge across tasks but also enhances the efficiency and robustness of graph classification models in biology.

翻译：图分类对于理解复杂生物系统至关重要，其中分子结构与相互作用天然地以图形式表示。传统图神经网络（GNNs）在静态任务中表现良好，但在动态环境下因灾难性遗忘问题而面临挑战。本文提出扰动稀疏化持续图学习（PSCGL），一种针对图数据分类（特别面向生物数据集）的稳健高效持续图学习框架。我们引入一种扰动采样策略以识别对模型学习有关键贡献的数据点，以及一种基于图模体的图稀疏化技术，在保持性能的同时降低存储需求。此外，我们的PSCGL框架内在具备防御图后门攻击的能力，这对敏感生物领域的应用至关重要。在生物数据集上的大量实验表明，PSCGL不仅能够跨任务保留知识，还能显著提升生物图分类模型的效率与稳健性。