Reinforcement Learning on Graph: A Survey

• URL: http://arxiv.org/abs/2204.06127v1
• Date: Wed, 13 Apr 2022 01:25:58 GMT
• Title: Reinforcement Learning on Graph: A Survey
• Authors: Nie Mingshuo, Chen Dongming, Wang Dongqi
• Abstract summary: We provide a comprehensive overview of RL models and graph mining and generalize these algorithms to Graph Reinforcement Learning (GRL) We discuss the applications of GRL methods across various domains and summarize the method description, open-source codes, and benchmark datasets of GRL methods. We propose possible important directions and challenges to be solved in the future.
• Score: 0.3867363075280544
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Graph mining tasks arise from many different application domains, ranging from social networks, transportation, E-commerce, etc., which have been receiving great attention from the theoretical and algorithm design communities in recent years, and there has been some pioneering work using the hotly researched reinforcement learning (RL) techniques to address graph data mining tasks. However, these graph mining algorithms and RL models are dispersed in different research areas, which makes it hard to compare different algorithms with each other. In this survey, we provide a comprehensive overview of RL models and graph mining and generalize these algorithms to Graph Reinforcement Learning (GRL) as a unified formulation. We further discuss the applications of GRL methods across various domains and summarize the method description, open-source codes, and benchmark datasets of GRL methods. Finally, we propose possible important directions and challenges to be solved in the future. This is the latest work on a comprehensive survey of GRL literature, and this work provides a global view for researchers as well as a learning resource for researchers outside the domain. In addition, we create an online open-source for both interested researchers who want to enter this rapidly developing domain and experts who would like to compare GRL methods.

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