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. CL methods for graph data, however, 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 settings) for graph data, which are
naturally applied to many node-, link-, and graph-level problems. Regarding
(b), we provide 23 benchmark scenarios based on 14 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 in
evaluation. Using all above, we report extensive benchmark results of seven
graph CL methods. Compared to the latest benchmark for graph CL, using BeGin,
we cover three times more combinations of incremental settings and levels of
problems.

本文介绍了一种针对图数据的持续学习方法，提供了标准的增量设置和基准测试场景，并使用 BeGin 框架对七种图像 CL 方法进行评估，其组合情况是最新基准测试的三倍。