  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. Compared to them, however, CL methods for
graph data (graph CL) are relatively 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 node-, link-, and
graph-level problems, extending the previously explored scope. Regarding (b),
we provide 31 benchmark scenarios based on 20 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.
Regarding benchmark results, we cover 3X more combinations of incremental
settings and levels of problems than the latest benchmark. All assets for the
benchmark framework are publicly available at
https://github.com/ShinhwanKang/BeGin.
