BeGin: Extensive Benchmark Scenarios and An Easy-to-use Framework for Graph Continual Learning

Continual Learning (CL) is the process of learning ceaselessly a sequence of tasks. Most existing CL methods deal with independent data (e.g., images and text) for which many benchmark frameworks and results under standard experimental settings are available. However, CL methods for graph data (graph CL) are surprisingly underexplored because of (a) the lack of standard experimental settings, especially regarding how to deal with the dependency between instances, (b) the lack of benchmark datasets and scenarios, and (c) high complexity in implementation and evaluation due to the dependency. In this paper, regarding (a), we define four standard incremental settings (task-, class-, domain-, and time-incremental) for graph data, which are naturally applied to many node-, link-, and graph-level problems. Regarding (b), we provide 25 benchmark scenarios based on 15 real-world graphs. Regarding (c), we develop BeGin, an easy and fool-proof framework for graph CL. BeGin is easily extended since it is modularized with reusable modules for data processing, algorithm design, and evaluation. Especially, the evaluation module is completely separated from user code to eliminate potential mistakes. Using all the above, we report extensive benchmark results of 10 graph CL methods. Compared to the latest benchmark for graph CL, using BeGin, we cover 3x more combinations of incremental settings and levels of problems. All assets for the benchmark framework are available at https://github.com/ShinhwanKang/BeGin.