mlsanityData to 7 Oct 2026

Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective

arXiv 2003.00330 ↗First posted 29 Feb 2020cs.AI cs.CL cs.LGcited by 93

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Abstract (arXiv)

Neural-symbolic computing has now become the subject of interest of both academic and industry research laboratories. Graph Neural Networks (GNN) have been widely used in relational and symbolic domains, with widespread application of GNNs in combinatorial optimization, constraint satisfaction, relational reasoning and other scientific domains. The need for improved explainability, interpretability and trust of AI systems in general demands principled methodologies, as suggested by neural-symbolic computing. In this paper, we review the state-of-the-art on the use of GNNs as a model of neural-symbolic computing. This includes the application of GNNs in several domains as well as its relationship to current developments in neural-symbolic computing.

Key facts. Cited by 93 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Feb 2020.

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Citing papers per year: 2021: 17; 2022: 19; 2023: 24; 2024: 18; 2025: 11; 2026: 4.

93 papers.

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  1. 2021Combinatorial optimization and reasoning with graph neural networkscited by 267
  2. 2024Safeguarding Large Language Models: A Surveycited by 73
  3. 2021From Statistical Relational to Neurosymbolic Artificial Intelligence: a Surveycited by 72
  4. 2024Building Guardrails for Large Language Modelscited by 61
  5. 2023Coupling Large Language Models with Logic Programming for Robust and General Reasoning from Textcited by 59
  6. 2021Complex Knowledge Base Question Answering: A Surveycited by 52
  7. 2023ChatGPT for Shaping the Future of Dentistry: The Potential of Multi-Modal Large Language Modelcited by 40
  8. 2021A Survey on Neural-symbolic Learning Systemscited by 39
  9. 2021Learning General Optimal Policies with Graph Neural Networks: Expressive Power, Transparency, and Limitscited by 38
  10. 2022Is Neuro-Symbolic AI Meeting its Promise in Natural Language Processing? A Structured Reviewcited by 37
  11. 2022NeuPSL: Neural Probabilistic Soft Logiccited by 34
  12. 2022Towards Data-and Knowledge-Driven Artificial Intelligence: A Survey on Neuro-Symbolic Computingcited by 34
38 more
  1. 2022Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysiscited by 29
  2. 2022Survey on Graph Neural Network Acceleration: An Algorithmic Perspectivecited by 26
  3. 2021Techniques for Symbol Grounding with SATNetcited by 24
  4. 2023Neurosymbolic Reinforcement Learning and Planning: A Surveycited by 24
  5. 2024Towards Cognitive AI Systems: a Survey and Prospective on Neuro-Symbolic AIcited by 24
  6. 2023Neurosymbolic AI for Reasoning over Knowledge Graphs: A Surveycited by 21
  7. 2023Machine Unlearning: A Surveycited by 18
  8. 2023Controlling Continuous Relaxation for Combinatorial Optimizationcited by 16
  9. 2022A Comprehensive Survey on Distributed Training of Graph Neural Networkscited by 15
  10. 2023Deep Explainable Relational Reinforcement Learning: A Neuro-Symbolic Approachcited by 15
  11. 2024SymbolicAI: A framework for logic-based approaches combining generative models and solverscited by 15
  12. 2023Injecting Logical Constraints into Neural Networks via Straight-Through Estimatorscited by 12
  13. 2023Achieving Diversity in Counterfactual Explanations: a Review and Discussioncited by 11
  14. 2021Faster-LTN: a neuro-symbolic, end-to-end object detection architecturecited by 10
  15. 2023Graph Neural Networks Go Forward-Forwardcited by 8
  16. 2023Sample-Efficient Learning of Novel Visual Conceptscited by 8
  17. 2022Neuro-Symbolic Learning: Principles and Applications in Ophthalmologycited by 7
  18. 2024Towards Efficient Neuro-Symbolic AI: From Workload Characterization to Hardware Architecturecited by 7
  19. 2025Symbolic Knowledge Extraction and Injection with Sub-symbolic Predictors: A Systematic Literature Reviewcited by 7
  20. 2021A conditional, a fuzzy and a probabilistic interpretation of self-organising mapscited by 6
  21. 2021Inclusion of Domain-Knowledge into GNNs using Mode-Directed Inverse Entailmentcited by 6
  22. 2023Graph-based Knowledge Distillation: A survey and experimental evaluationcited by 6
  23. 2023Neurosymbolic AI and its Taxonomy: a surveycited by 6
  24. 2024Neural Model Checkingcited by 6
  25. 2024Neurosymbolic Graph Enrichment for Grounded World Modelscited by 6
  26. 2025Unlocking the Potential of Generative AI through Neuro-Symbolic Architectures: Benefits and Limitationscited by 6
  27. 2021KANDINSKYPatterns -- An experimental exploration environment for Pattern Analysis and Machine Intelligencecited by 5
  28. 2021Natlog: a Lightweight Logic Programming Language with a Neuro-symbolic Touchcited by 5
  29. 2021Can Graph Neural Networks Learn to Solve MaxSAT Problem?cited by 5
  30. 2022Reinforcement Learning in Practice: Opportunities and Challengescited by 5
  31. 2022Differentiable Inference of Temporal Logic Formulascited by 5
  32. 2024Using Domain Knowledge to Guide Dialog Structure Induction via Neural Probabilistic Soft Logiccited by 5
  33. 2025A Study on Neuro-Symbolic Artificial Intelligence: Healthcare Perspectivescited by 5
  34. 2025Position: Foundation Models for Tabular Data within Systemic Contexts Need Groundingcited by 5
  35. 2021Solving Machine Learning Problemscited by 4
  36. 2022Building Human-like Communicative Intelligence: A Grounded Perspectivecited by 4
  37. 2022An ASP approach for reasoning on neural networks under a finitely many-valued semantics for weighted conditional knowledge basescited by 4
  38. 2023The Roles of Symbols in Neural-based AI: They are Not What You Think!cited by 4

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