Title: On Making Graph Continual Learning Easy, Fool-Proof, and Extensive: a Benchmark Framework and Scenarios. Abstract: 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-, edge-, and graph-level problems. Regarding (b), we provide 17 benchmark scenarios based on nine 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, validation, 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 can cover 2.75× more combinations of incremental settings and levels of problems, and, we can implement the same the graph CL method with about 30% fewer lines of code.