Universal Graph Continual Learning
Thanh Duc Hoang Do Viet Tung Duy-Hung Nguyen Bao-Sinh Nguyen Huy Hoang Nguyen Hung Le
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.