  Research on continual learning (CL) mainly focuses on data represented in the
Euclidean space, while research on graph-structured data is scarce.
Furthermore, most graph learning models are tailored for static graphs.
However, graphs usually evolve continually in the real world. Catastrophic
forgetting also emerges in graph learning models when being trained
incrementally. This leads to the need to develop robust, effective and
efficient continual graph learning approaches. Continual graph learning (CGL)
is an emerging area aiming to realize continual learning on graph-structured
data. This survey is written to shed light on this emerging area. It introduces
the basic concepts of CGL and highlights two unique challenges brought by
graphs. Then it reviews and categorizes recent state-of-the-art approaches,
analyzing their strategies to tackle the unique challenges in CGL. Besides, it
discusses the main concerns in each family of CGL methods, offering potential
solutions. Finally, it explores the open issues and potential applications of
CGL.
