  Recently, continual graph learning has been increasingly adopted for diverse
graph-structured data processing tasks in non-stationary environments. Despite
its promising learning capability, current studies on continual graph learning
mainly focus on mitigating the catastrophic forgetting problem while ignoring
continuous performance improvement. To bridge this gap, this article aims to
provide a comprehensive survey of recent efforts on continual graph learning.
Specifically, we introduce a new taxonomy of continual graph learning from the
perspective of overcoming catastrophic forgetting. Moreover, we systematically
analyze the challenges of applying these continual graph learning methods in
improving performance continuously and then discuss the possible solutions.
Finally, we present open issues and future directions pertaining to the
development of continual graph learning and discuss how they impact continuous
performance improvement.
