  Graph Neural Networks (GNNs) have become the backbone for a myriad of tasks
pertaining to graphs and similar topological data structures. While many works
have been established in domains related to node and graph
classification/regression tasks, they mostly deal with a single task. Continual
learning on graphs is largely unexplored and existing graph continual learning
approaches are limited to the task-incremental learning scenarios. This paper
proposes a graph continual learning strategy that combines the
architecture-based and memory-based approaches. The structural learning
strategy is driven by reinforcement learning, where a controller network is
trained in such a way to determine an optimal number of nodes to be
added/pruned from the base network when new tasks are observed, thus assuring
sufficient network capacities. The parameter learning strategy is underpinned
by the concept of Dark Experience replay method to cope with the catastrophic
forgetting problem. Our approach is numerically validated with several graph
continual learning benchmark problems in both task-incremental learning and
class-incremental learning settings. Compared to recently published works, our
approach demonstrates improved performance in both the settings. The
implementation code can be found at \url{https://github.com/codexhammer/gcl}.
