mlsanityData to 7 Oct 2026

Reinforcement learning on graphs: A survey

arXiv 2204.06127 ↗First posted 13 Apr 2022cs.LGcited by 18

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Abstract (arXiv)

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.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 102 of its 222 references that are in the map. First posted on arXiv 13 Apr 2022.

Builds on

102 of 222 references.

References · 102

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38 more
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Citing papers per year: 2023: 4; 2024: 7; 2025: 3; 2026: 4.

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  1. 2023Multimodal Data Integration for Oncology in the Era of Deep Neural Networks: A Reviewcited by 20
  2. 2024The RL/LLM Taxonomy Tree: Reviewing Synergies Between Reinforcement Learning and Large Language Modelscited by 15
  3. 2024Towards Generalizability of Multi-Agent Reinforcement Learning in Graphs with Recurrent Message Passingcited by 8
  4. 2023Reinforcement Neighborhood Selection for Unsupervised Graph Anomaly Detectioncited by 7
  5. 2024Graph Reinforcement Learning for Power Grids: A Comprehensive Surveycited by 6
  6. 2023A Comprehensive Survey on Graph Summarization with Graph Neural Networkscited by 3
  7. 2024Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligencecited by 2
  8. 2023From Images to Connections: Can DQN with GNNs learn the Strategic Game of Hex?cited by 1
  9. 2025Agentic-KGR: Co-evolutionary Knowledge Graph Construction through Multi-Agent Reinforcement Learningcited by 1
  10. 2024TARN-VIST: Topic Aware Reinforcement Network for Visual Storytellingcited by 0
  11. 2024Balanced Edge Pruning for Graph Anomaly Detection with Noisy Labelscited by 0
  12. 2024Dynamic Weight Adjusting Deep Q-Networks for Real-Time Environmental Adaptationcited by 0
6 more
  1. 2025Metric spaces of walks and Lipschitz duality on graphscited by 0
  2. 2025Leveraging Vulnerabilities in Temporal Graph Neural Networks via Strategic High-Impact Assaultscited by 0
  3. 2026Boosting Deep Reinforcement Learning with Semantic Knowledge for Robotic Manipulatorscited by 0
  4. 2026EUPHORIA: Efficient Universal Planning via Hybrid Optimization for Robust Industrial Robotic Assemblycited by 0
  5. 2026Projecting Latent RL Actions: Towards Generalizable and Scalable Graph Combinatorial Optimizationcited by 0
  6. 2026HiGFRL: Hierarchical Graph Fusion-Driven Reinforcement Learning for Dependency-Aware Task Scheduling in Heterogeneous Cloudcited by 0