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.

深度强化学习（DRL）赋予了各种人工智能领域，包括模式识别，机器人技术，推荐系统和游戏。同样，图神经网络（GNN）也证明了它们在图形结构数据的监督学习方面的出色表现。最近，GNN与DRL用于图形结构环境的融合引起了很多关注。本文对这些混合动力作品进行了全面评论。这些作品可以分为两类：（1）算法增强，其中DRL和GNN相互补充以获得更好的实用性； （2）特定于应用程序的增强，其中DRL和GNN相互支持。这种融合有效地解决了工程和生命科学方面的各种复杂问题。基于审查，我们进一步分析了融合这两个领域的适用性和好处，尤其是在提高通用性和降低计算复杂性方面。最后，集成DRL和GNN的关键挑战以及潜在的未来研究方向被突出显示，这将引起更广泛的机器学习社区的关注。

Influence Maximization (IM) is a classical combinatorial optimization problem, which can be widely used in mobile networks, social computing, and recommendation systems. It aims at selecting a small number of users such that maximizing the influence spread across the online social network. Because of its potential commercial and academic value, there are a lot of researchers focusing on studying the IM problem from different perspectives. The main challenge comes from the NP-hardness of the IM problem and \#P-hardness of estimating the influence spread, thus traditional algorithms for overcoming them can be categorized into two classes: heuristic algorithms and approximation algorithms. However, there is no theoretical guarantee for heuristic algorithms, and the theoretical design is close to the limit. Therefore, it is almost impossible to further optimize and improve their performance. With the rapid development of artificial intelligence, the technology based on Machine Learning (ML) has achieved remarkable achievements in many fields. In view of this, in recent years, a number of new methods have emerged to solve combinatorial optimization problems by using ML-based techniques. These methods have the advantages of fast solving speed and strong generalization ability to unknown graphs, which provide a brand-new direction for solving combinatorial optimization problems. Therefore, we abandon the traditional algorithms based on iterative search and review the recent development of ML-based methods, especially Deep Reinforcement Learning, to solve the IM problem and other variants in social networks. We focus on summarizing the relevant background knowledge, basic principles, common methods, and applied research. Finally, the challenges that need to be solved urgently in future IM research are pointed out.

社交机器人被称为社交网络上的自动帐户，这些帐户试图像人类一样行事。尽管图形神经网络（GNNS）已大量应用于社会机器人检测领域，但大量的领域专业知识和先验知识大量参与了最先进的方法，以设计专门的神经网络体系结构，以设计特定的神经网络体系结构。分类任务。但是，在模型设计中涉及超大的节点和网络层，通常会导致过度平滑的问题和缺乏嵌入歧视。在本文中，我们提出了罗斯加斯（Rosgas），这是一种新颖的加强和自我监督的GNN Architecture搜索框架，以适应性地指出了最合适的多跳跃社区和GNN体系结构中的层数。更具体地说，我们将社交机器人检测问题视为以用户为中心的子图嵌入和分类任务。我们利用异构信息网络来通过利用帐户元数据，关系，行为特征和内容功能来展示用户连接。 Rosgas使用多代理的深钢筋学习（RL）机制来导航最佳邻域和网络层的搜索，以分别学习每个目标用户的子图嵌入。开发了一种用于加速RL训练过程的最接近的邻居机制，Rosgas可以借助自我监督的学习来学习更多的判别子图。 5个Twitter数据集的实验表明，Rosgas在准确性，训练效率和稳定性方面优于最先进的方法，并且在处理看不见的样本时具有更好的概括。

The stock market prediction has been a traditional yet complex problem researched within diverse research areas and application domains due to its non-linear, highly volatile and complex nature. Existing surveys on stock market prediction often focus on traditional machine learning methods instead of deep learning methods. Deep learning has dominated many domains, gained much success and popularity in recent years in stock market prediction. This motivates us to provide a structured and comprehensive overview of the research on stock market prediction focusing on deep learning techniques. We present four elaborated subtasks of stock market prediction and propose a novel taxonomy to summarize the state-of-the-art models based on deep neural networks from 2011 to 2022. In addition, we also provide detailed statistics on the datasets and evaluation metrics commonly used in the stock market. Finally, we highlight some open issues and point out several future directions by sharing some new perspectives on stock market prediction.

未来的互联网涉及几种新兴技术，例如5G和5G网络，车辆网络，无人机（UAV）网络和物联网（IOT）。此外，未来的互联网变得异质并分散了许多相关网络实体。每个实体可能需要做出本地决定，以在动态和不确定的网络环境下改善网络性能。最近使用标准学习算法，例如单药强化学习（RL）或深入强化学习（DRL），以使每个网络实体作为代理人通过与未知环境进行互动来自适应地学习最佳决策策略。但是，这种算法未能对网络实体之间的合作或竞争进行建模，而只是将其他实体视为可能导致非平稳性问题的环境的一部分。多机构增强学习（MARL）允许每个网络实体不仅观察环境，还可以观察其他实体的政策来学习其最佳政策。结果，MAL可以显着提高网络实体的学习效率，并且最近已用于解决新兴网络中的各种问题。在本文中，我们因此回顾了MAL在新兴网络中的应用。特别是，我们提供了MARL的教程，以及对MARL在下一代互联网中的应用进行全面调查。特别是，我们首先介绍单代机Agent RL和MARL。然后，我们回顾了MAL在未来互联网中解决新兴问题的许多应用程序。这些问题包括网络访问，传输电源控制，计算卸载，内容缓存，数据包路由，无人机网络的轨迹设计以及网络安全问题。

Reinforcement Learning (RL) is a popular machine learning paradigm where intelligent agents interact with the environment to fulfill a long-term goal. Driven by the resurgence of deep learning, Deep RL (DRL) has witnessed great success over a wide spectrum of complex control tasks. Despite the encouraging results achieved, the deep neural network-based backbone is widely deemed as a black box that impedes practitioners to trust and employ trained agents in realistic scenarios where high security and reliability are essential. To alleviate this issue, a large volume of literature devoted to shedding light on the inner workings of the intelligent agents has been proposed, by constructing intrinsic interpretability or post-hoc explainability. In this survey, we provide a comprehensive review of existing works on eXplainable RL (XRL) and introduce a new taxonomy where prior works are clearly categorized into model-explaining, reward-explaining, state-explaining, and task-explaining methods. We also review and highlight RL methods that conversely leverage human knowledge to promote learning efficiency and performance of agents while this kind of method is often ignored in XRL field. Some challenges and opportunities in XRL are discussed. This survey intends to provide a high-level summarization of XRL and to motivate future research on more effective XRL solutions. Corresponding open source codes are collected and categorized at https://github.com/Plankson/awesome-explainable-reinforcement-learning.

Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to speech recognition and natural language understanding. The data in these tasks are typically represented in the Euclidean space. However, there is an increasing number of applications where data are generated from non-Euclidean domains and are represented as graphs with complex relationships and interdependency between objects. The complexity of graph data has imposed significant challenges on existing machine learning algorithms. Recently, many studies on extending deep learning approaches for graph data have emerged. In this survey, we provide a comprehensive overview of graph neural networks (GNNs) in data mining and machine learning fields. We propose a new taxonomy to divide the state-of-the-art graph neural networks into four categories, namely recurrent graph neural networks, convolutional graph neural networks, graph autoencoders, and spatial-temporal graph neural networks. We further discuss the applications of graph neural networks across various domains and summarize the open source codes, benchmark data sets, and model evaluation of graph neural networks. Finally, we propose potential research directions in this rapidly growing field.

Proper functioning of connected and automated vehicles (CAVs) is crucial for the safety and efficiency of future intelligent transport systems. Meanwhile, transitioning to fully autonomous driving requires a long period of mixed autonomy traffic, including both CAVs and human-driven vehicles. Thus, collaboration decision-making for CAVs is essential to generate appropriate driving behaviors to enhance the safety and efficiency of mixed autonomy traffic. In recent years, deep reinforcement learning (DRL) has been widely used in solving decision-making problems. However, the existing DRL-based methods have been mainly focused on solving the decision-making of a single CAV. Using the existing DRL-based methods in mixed autonomy traffic cannot accurately represent the mutual effects of vehicles and model dynamic traffic environments. To address these shortcomings, this article proposes a graph reinforcement learning (GRL) approach for multi-agent decision-making of CAVs in mixed autonomy traffic. First, a generic and modular GRL framework is designed. Then, a systematic review of DRL and GRL methods is presented, focusing on the problems addressed in recent research. Moreover, a comparative study on different GRL methods is further proposed based on the designed framework to verify the effectiveness of GRL methods. Results show that the GRL methods can well optimize the performance of multi-agent decision-making for CAVs in mixed autonomy traffic compared to the DRL methods. Finally, challenges and future research directions are summarized. This study can provide a valuable research reference for solving the multi-agent decision-making problems of CAVs in mixed autonomy traffic and can promote the implementation of GRL-based methods into intelligent transportation systems. The source code of our work can be found at https://github.com/Jacklinkk/Graph_CAVs.

组合优化是运营研究和计算机科学领域的一个公认领域。直到最近，它的方法一直集中在孤立地解决问题实例，而忽略了它们通常源于实践中的相关数据分布。但是，近年来，人们对使用机器学习，尤其是图形神经网络（GNN）的兴趣激增，作为组合任务的关键构件，直接作为求解器或通过增强确切的求解器。GNN的电感偏差有效地编码了组合和关系输入，因为它们对排列和对输入稀疏性的意识的不变性。本文介绍了对这个新兴领域的最新主要进步的概念回顾，旨在优化和机器学习研究人员。

With the development of deep representation learning, the domain of reinforcement learning (RL) has become a powerful learning framework now capable of learning complex policies in high dimensional environments. This review summarises deep reinforcement learning (DRL) algorithms and provides a taxonomy of automated driving tasks where (D)RL methods have been employed, while addressing key computational challenges in real world deployment of autonomous driving agents. It also delineates adjacent domains such as behavior cloning, imitation learning, inverse reinforcement learning that are related but are not classical RL algorithms. The role of simulators in training agents, methods to validate, test and robustify existing solutions in RL are discussed.

Deep learning has been shown to be successful in a number of domains, ranging from acoustics, images, to natural language processing. However, applying deep learning to the ubiquitous graph data is non-trivial because of the unique characteristics of graphs. Recently, substantial research efforts have been devoted to applying deep learning methods to graphs, resulting in beneficial advances in graph analysis techniques. In this survey, we comprehensively review the different types of deep learning methods on graphs. We divide the existing methods into five categories based on their model architectures and training strategies: graph recurrent neural networks, graph convolutional networks, graph autoencoders, graph reinforcement learning, and graph adversarial methods. We then provide a comprehensive overview of these methods in a systematic manner mainly by following their development history. We also analyze the differences and compositions of different methods. Finally, we briefly outline the applications in which they have been used and discuss potential future research directions.

通信网络是当代社会中的重要基础设施。仍存在许多挑战，在该活性研究区域中不断提出新的解决方案。近年来，为了模拟网络拓扑，基于图形的深度学习在通信网络中的一系列问题中实现了最先进的性能。在本调查中，我们使用基于不同的图形的深度学习模型来审查快速增长的研究机构，例如，使用不同的图形深度学习模型。图表卷积和曲线图注意网络，在不同类型的通信网络中的各种问题中，例如，无线网络，有线网络和软件定义的网络。我们还为每项研究提供了一个有组织的问题和解决方案列表，并确定了未来的研究方向。据我们所知，本文是第一个专注于在涉及有线和无线场景的通信网络中应用基于图形的深度学习方法的调查。要跟踪后续研究，创建了一个公共GitHub存储库，其中相关文件将不断更新。

生物医学网络是与疾病网络的蛋白质相互作用的普遍描述符，从蛋白质相互作用，一直到医疗保健系统和科学知识。随着代表学习提供强大的预测和洞察的显着成功，我们目睹了表现形式学习技术的快速扩展，进入了这些网络的建模，分析和学习。在这篇综述中，我们提出了一个观察到生物学和医学中的网络长期原则 - 而在机器学习研究中经常出口 - 可以为代表学习提供概念基础，解释其当前的成功和限制，并告知未来进步。我们综合了一系列算法方法，即在其核心利用图形拓扑到将网络嵌入到紧凑的向量空间中，并捕获表示陈述学习证明有用的方式的广度。深远的影响包括鉴定复杂性状的变异性，单细胞的异心行为及其对健康的影响，协助患者的诊断和治疗以及制定安全有效的药物。

Transformer, originally devised for natural language processing, has also attested significant success in computer vision. Thanks to its super expressive power, researchers are investigating ways to deploy transformers to reinforcement learning (RL) and the transformer-based models have manifested their potential in representative RL benchmarks. In this paper, we collect and dissect recent advances on transforming RL by transformer (transformer-based RL or TRL), in order to explore its development trajectory and future trend. We group existing developments in two categories: architecture enhancement and trajectory optimization, and examine the main applications of TRL in robotic manipulation, text-based games, navigation and autonomous driving. For architecture enhancement, these methods consider how to apply the powerful transformer structure to RL problems under the traditional RL framework, which model agents and environments much more precisely than deep RL methods, but they are still limited by the inherent defects of traditional RL algorithms, such as bootstrapping and "deadly triad". For trajectory optimization, these methods treat RL problems as sequence modeling and train a joint state-action model over entire trajectories under the behavior cloning framework, which are able to extract policies from static datasets and fully use the long-sequence modeling capability of the transformer. Given these advancements, extensions and challenges in TRL are reviewed and proposals about future direction are discussed. We hope that this survey can provide a detailed introduction to TRL and motivate future research in this rapidly developing field.

Deep reinforcement learning is poised to revolutionise the field of AI and represents a step towards building autonomous systems with a higher level understanding of the visual world. Currently, deep learning is enabling reinforcement learning to scale to problems that were previously intractable, such as learning to play video games directly from pixels. Deep reinforcement learning algorithms are also applied to robotics, allowing control policies for robots to be learned directly from camera inputs in the real world. In this survey, we begin with an introduction to the general field of reinforcement learning, then progress to the main streams of value-based and policybased methods. Our survey will cover central algorithms in deep reinforcement learning, including the deep Q-network, trust region policy optimisation, and asynchronous advantage actor-critic. In parallel, we highlight the unique advantages of deep neural networks, focusing on visual understanding via reinforcement learning. To conclude, we describe several current areas of research within the field.

深度强化学习（DRL）和深度多机构的强化学习（MARL）在包括游戏AI，自动驾驶汽车，机器人技术等各种领域取得了巨大的成功。但是，众所周知，DRL和Deep MARL代理的样本效率低下，即使对于相对简单的问题设置，通常也需要数百万个相互作用，从而阻止了在实地场景中的广泛应用和部署。背后的一个瓶颈挑战是众所周知的探索问题，即如何有效地探索环境和收集信息丰富的经验，从而使政策学习受益于最佳研究。在稀疏的奖励，吵闹的干扰，长距离和非平稳的共同学习者的复杂环境中，这个问题变得更加具有挑战性。在本文中，我们对单格和多代理RL的现有勘探方法进行了全面的调查。我们通过确定有效探索的几个关键挑战开始调查。除了上述两个主要分支外，我们还包括其他具有不同思想和技术的著名探索方法。除了算法分析外，我们还对一组常用基准的DRL进行了全面和统一的经验比较。根据我们的算法和实证研究，我们终于总结了DRL和Deep Marl中探索的公开问题，并指出了一些未来的方向。

互联网连接系统的规模大大增加，这些系统比以往任何时候都更接触到网络攻击。网络攻击的复杂性和动态需要保护机制响应，自适应和可扩展。机器学习，或更具体地说，深度增强学习（DRL），方法已经广泛提出以解决这些问题。通过将深入学习纳入传统的RL，DRL能够解决复杂，动态，特别是高维的网络防御问题。本文提出了对为网络安全开发的DRL方法进行了调查。我们触及不同的重要方面，包括基于DRL的网络 - 物理系统的安全方法，自主入侵检测技术和基于多元的DRL的游戏理论模拟，用于防范策略对网络攻击。还给出了对基于DRL的网络安全的广泛讨论和未来的研究方向。我们预计这一全面审查提供了基础，并促进了未来的研究，探讨了越来越复杂的网络安全问题。

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.

数字化和远程连接扩大了攻击面，使网络系统更脆弱。由于攻击者变得越来越复杂和资源丰富，仅仅依赖传统网络保护，如入侵检测，防火墙和加密，不足以保护网络系统。网络弹性提供了一种新的安全范式，可以使用弹性机制来补充保护不足。一种网络弹性机制（CRM）适应了已知的或零日威胁和实际威胁和不确定性，并对他们进行战略性地响应，以便在成功攻击时保持网络系统的关键功能。反馈架构在启用CRM的在线感应，推理和致动过程中发挥关键作用。强化学习（RL）是一个重要的工具，对网络弹性的反馈架构构成。它允许CRM提供有限或没有事先知识和攻击者的有限攻击的顺序响应。在这项工作中，我们审查了Cyber​​恢复力的RL的文献，并讨论了对三种主要类型的漏洞，即姿势有关，与信息相关的脆弱性的网络恢复力。我们介绍了三个CRM的应用领域：移动目标防御，防守网络欺骗和辅助人类安全技术。 RL算法也有漏洞。我们解释了RL的三个漏洞和目前的攻击模型，其中攻击者针对环境与代理商之间交换的信息：奖励，国家观察和行动命令。我们展示攻击者可以通过最低攻击努力来欺骗RL代理商学习邪恶的政策。最后，我们讨论了RL为基于RL的CRM的网络安全和恢复力和新兴应用的未来挑战。