Lifelong Graph Learning

Graph neural networks are powerful models for many graph-structured tasks. In this paper, we aim to solve the problem of lifelong learning for graph neural networks. One of the main challenges is the effect of "catastrophic forgetting" for continuously learning a sequence of tasks, as the nodes can only be present to the model once. Moreover, the number of nodes changes dynamically in lifelong learning and this makes many graph models and sampling strategies inapplicable. To solve these problems, we construct a new graph topology, called the feature graph. It takes features as new nodes and turns nodes into independent graphs. This successfully converts the original problem of node classification to graph classification. In this way, the increasing nodes in lifelong learning can be regarded as increasing training samples, which makes lifelong learning easier. We demonstrate that the feature graph achieves much higher accuracy than the state-of-the-art methods in both data-incremental and class-incremental tasks. We expect that the feature graph will have broad potential applications for graph-structured tasks in lifelong learning.