Continual learning on graphs tackles the problem of training a graph neural
network (GNN) where graph data arrive in a streaming fashion and the model
tends to forget knowledge from previous tasks when updating with new data.
Traditional continual learning strategies such as Experience Replay can be
adapted to streaming graphs, however, these methods often face challenges such
as inefficiency in preserving graph topology and incapability of capturing the
correlation between old and new tasks. To address these challenges, we propose
TA$\mathbb{CO}$, a (t)opology-(a)ware graph (co)arsening and (co)ntinual
learning framework that stores information from previous tasks as a reduced
graph. At each time period, this reduced graph expands by combining with a new
graph and aligning shared nodes, and then it undergoes a "zoom out" process by
reduction to maintain a stable size. We design a graph coarsening algorithm
based on node representation proximities to efficiently reduce a graph and
preserve topological information. We empirically demonstrate the learning
process on the reduced graph can approximate that of the original graph. Our
experiments validate the effectiveness of the proposed framework on three
real-world datasets using different backbone GNN models.

提出了一种基于拓扑感知的图粗化与持续学习框架，该框架通过将先前任务的信息存储为精简图，在每个时间段通过与新图结合并对齐共享节点来扩展精简图，并通过缩减过程进行稳定大小的 “缩小”，实验证明了该框架在三个真实数据集上使用不同的骨干 GNN 模型的有效性。