Many real-world graph learning tasks require handling dynamic graphs where new nodes and edges emerge. Dynamic graph learning methods commonly suffer from the catastrophic forgetting problem, where knowledge learned for previous graphs is overwritten by updates for new graphs. To alleviate the problem, continual graph learning methods are proposed. However, existing continual graph learning methods aim to learn new patterns and maintain old ones with the same set of parameters of fixed size, and thus face a fundamental tradeoff between both goals. In this paper, we propose Parameter Isolation GNN (PI-GNN) for continual learning on dynamic graphs that circumvents the tradeoff via parameter isolation and expansion. Our motivation lies in that different parameters contribute to learning different graph patterns. Based on the idea, we expand model parameters to continually learn emerging graph patterns. Meanwhile, to effectively preserve knowledge for unaffected patterns, we find parameters that correspond to them via optimization and freeze them to prevent them from being rewritten. Experiments on eight real-world datasets corroborate the effectiveness of PI-GNN compared to state-of-the-art baselines.

翻译：许多现实世界的图学习任务需要处理动态图，其中新节点和边不断涌现。动态图学习方法普遍存在灾难性遗忘问题，即先前图学习到的知识会被新图更新所覆盖。为缓解该问题，研究者提出了持续图学习方法。然而，现有持续图学习方法旨在使用固定大小的同一组参数来学习新模式并保持旧模式，因此面临两者之间的根本性权衡。本文提出基于参数隔离的图神经网络（PI-GNN）用于动态图持续学习，该方法通过参数隔离与扩展规避了上述权衡。我们的动机在于：不同参数对学习不同图模式的贡献存在差异。基于这一思想，我们扩展模型参数以持续学习新兴图模式。同时，为有效保留未受影响模式的知识，我们通过优化定位与这些模式对应的参数，并通过冻结操作防止其被重写。在八个真实数据集上的实验证实，与最先进的基线方法相比，PI-GNN具有显著有效性。