  Graph learning is a popular approach for performing machine learning on
graph-structured data. It has revolutionized the machine learning ability to
model graph data to address downstream tasks. Its application is wide due to
the availability of graph data ranging from all types of networks to
information systems. Most graph learning methods assume that the graph is
static and its complete structure is known during training. This limits their
applicability since they cannot be applied to problems where the underlying
graph grows over time and/or new tasks emerge incrementally. Such applications
require a lifelong learning approach that can learn the graph continuously and
accommodate new information whilst retaining previously learned knowledge.
Lifelong learning methods that enable continuous learning in regular domains
like images and text cannot be directly applied to continuously evolving graph
data, due to its irregular structure. As a result, graph lifelong learning is
gaining attention from the research community. This survey paper provides a
comprehensive overview of recent advancements in graph lifelong learning,
including the categorization of existing methods, and the discussions of
potential applications and open research problems.
