We address catastrophic forgetting issues in graph learning as incoming data
transits from one to another graph distribution. Whereas prior studies
primarily tackle one setting of graph continual learning such as incremental
node classification, we focus on a universal approach wherein each data point
in a task can be a node or a graph, and the task varies from node to graph
classification. We propose a novel method that enables graph neural networks to
excel in this universal setting. Our approach perseveres knowledge about past
tasks through a rehearsal mechanism that maintains local and global structure
consistency across the graphs. We benchmark our method against various
continual learning baselines in real-world graph datasets and achieve
significant improvement in average performance and forgetting across tasks.

我们提出了一种针对图学习中的灾难性遗忘问题的方法，在数据从一个图分布过渡到另一个图分布时，通过维持图的局部和全局结构一致性的排练机制来保持对过去任务的知识，并在实际图数据集中与各种持续学习基线进行了性能评测，取得了平均性能和任务遗忘方面的显著改进。