Reinforcement learning on graphs: A survey

TL;DR

This survey provides a comprehensive overview of how reinforcement learning techniques are applied to graph data mining, unifying various methods, discussing applications, and highlighting future challenges in Graph Reinforcement Learning.

Contribution

It offers the first unified formulation of Graph Reinforcement Learning, summarizes existing methods, and provides open-source resources and benchmarks for the community.

Findings

Unified framework for GRL methods

Summary of applications across domains

Open-source datasets and benchmarks

Abstract

Graph mining tasks arise from many different application domains, ranging from social networks, transportation to E-commerce, etc., which have been receiving great attention from the theoretical and algorithmic design communities in recent years, and there has been some pioneering work employing the research-rich Reinforcement Learning (RL) techniques to address graph data mining tasks. However, these graph mining methods and RL models are dispersed in different research areas, which makes it hard to compare them. In this survey, we provide a comprehensive overview of RL and graph mining methods and generalize these methods to Graph Reinforcement Learning (GRL) as a unified formulation. We further discuss the applications of GRL methods across various domains and summarize the method descriptions, open-source codes, and benchmark datasets of GRL methods. Furthermore, we propose important directions and challenges to be solved in the future. As far as we know, this is the latest work on a comprehensive survey of GRL, this work provides a global view and a learning resource for scholars. In addition, we create an online open-source for both interested scholars who want to enter this rapidly developing domain and experts who would like to compare GRL methods.