Continual Learning on Graphs: A Survey

TL;DR

This survey reviews recent advancements in continual graph learning, highlighting challenges in overcoming catastrophic forgetting and emphasizing the need for continuous performance improvement in non-stationary environments.

Contribution

It introduces a new taxonomy for continual graph learning and systematically analyzes challenges and solutions for improving performance beyond catastrophic forgetting.

Findings

Provides a comprehensive taxonomy of continual graph learning methods.

Analyzes challenges in continuous performance improvement.

Discusses open issues and future research directions.

Abstract

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.