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.
翻译:图表采矿任务来自许多不同的应用领域,从社会网络、交通到电子商务等,近年来,这些应用领域一直受到理论和算法设计界的极大关注,而且已经开展了一些开拓性工作,利用研究丰富的强化学习技术解决图表数据采矿任务,然而,这些图表采矿方法和RL模型分散在不同研究领域,难以加以比较。在本次调查中,我们全面概述了RL和图表采矿方法,并将这些方法概括为统一表述。我们进一步讨论了GRL方法在各个领域的应用情况,并总结了方法说明、开源代码和GRL方法的基准数据集。此外,我们提出了今后要解决的重要方向和挑战。据我们所知,这是关于GRL综合调查的最新工作,这项工作为学者提供了全球视角和学习资源。此外,我们为有兴趣的学者和愿意进入这一迅速发展的域的专家以及愿意比较GRL方法的专家创建了在线开放源。