计算机科学
终身学习
分类
图形数据库
图形
人工智能
机器学习
理论计算机科学
心理学
教育学
作者
Falih Gozi Febrinanto,Feng Xia,Kristen Moore,Chandra Thapa,Charu C. Aggarwal
出处
期刊:IEEE Computational Intelligence Magazine
[Institute of Electrical and Electronics Engineers]
日期:2023-01-25
卷期号:18 (1): 32-51
被引量:24
标识
DOI:10.1109/mci.2022.3222049
摘要
Graph learning is a popular approach for perfor ming 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.
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