Graph Lifelong Learning: A Survey

TL;DR

This survey reviews recent advancements in graph lifelong learning, focusing on methods that enable continuous, incremental learning on evolving graph data, addressing challenges unique to graph structures.

Contribution

It categorizes existing graph lifelong learning methods, discusses potential applications, and highlights open research problems in this emerging field.

Findings

Recent methods enable continuous learning on evolving graphs

Graph lifelong learning addresses issues of incremental data and task emergence

Open problems include scalability and handling dynamic graph changes

Abstract

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.