mlsanityData to 7 Oct 2026

Learning Combinatorial Optimization on Graphs: A Survey with Applications to Networking

arXiv 2005.11081 ↗First posted 22 May 2020cs.LG cs.AI stat.MLcited by 70

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Abstract (arXiv)

Existing approaches to solving combinatorial optimization problems on graphs suffer from the need to engineer each problem algorithmically, with practical problems recurring in many instances. The practical side of theoretical computer science, such as computational complexity, then needs to be addressed. Relevant developments in machine learning research on graphs are surveyed for this purpose. We organize and compare the structures involved with learning to solve combinatorial optimization problems, with a special eye on the telecommunications domain and its continuous development of live and research networks.

Key facts. Cited by 70 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 May 2020.

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Citing papers per year: 2021: 20; 2022: 15; 2023: 14; 2024: 6; 2025: 8; 2026: 7.

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  1. 2021Combinatorial optimization and reasoning with graph neural networkscited by 267
  2. 2023Neural Combinatorial Optimization with Heavy Decoder: Toward Large Scale Generalizationcited by 140
  3. 2022DIMES: A Differentiable Meta Solver for Combinatorial Optimization Problemscited by 127
  4. 2021Efficient Active Search for Combinatorial Optimization Problemscited by 99
  5. 2021Deep Policy Dynamic Programming for Vehicle Routing Problemscited by 94
  6. 2022Self-Supervised Primal-Dual Learning for Constrained Optimizationcited by 60
  7. 2022Pareto Set Learning for Neural Multi-objective Combinatorial Optimizationcited by 56
  8. 2021Constructions in combinatorics via neural networkscited by 46
  9. 2021Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustnesscited by 35
  10. 2023Equivariant Deep Weight Space Alignmentcited by 34
  11. 2024Self-Improvement for Neural Combinatorial Optimization: Sample without Replacement, but Improvementcited by 32
  12. 2023The Expressive Power of Graph Neural Networks: A Surveycited by 27
38 more
  1. 2021An Overview and Experimental Study of Learning-based Optimization Algorithms for Vehicle Routing Problemcited by 22
  2. 2021Graph Neural Networks for Communication Networks: Context, Use Cases and Opportunitiescited by 22
  3. 2021Learning Hard Optimization Problems: A Data Generation Perspectivecited by 21
  4. 2021Meta-Learning-Based Deep Reinforcement Learning for Multiobjective Optimization Problemscited by 20
  5. 2021Fast Approximations for Job Shop Scheduling: A Lagrangian Dual Deep Learning Methodcited by 20
  6. 2025Pareto Set Learning for Multi-Objective Reinforcement Learningcited by 18
  7. 2021Guidelines for the Computational Testing of Machine Learning approaches to Vehicle Routing Problemscited by 16
  8. 2022Learning to Solve Vehicle Routing Problems: A Surveycited by 16
  9. 2022Learning to Solve Combinatorial Graph Partitioning Problems via Efficient Explorationcited by 14
  10. 2021Deep Reinforcement Learning for Combinatorial Optimization: Covering Salesman Problemscited by 13
  11. 2021MODRL/D-EL: Multiobjective Deep Reinforcement Learning with Evolutionary Learning for Multiobjective Optimizationcited by 8
  12. 2022LeNSE: Learning To Navigate Subgraph Embeddings for Large-Scale Combinatorial Optimisationcited by 8
  13. 2023Deep reinforcement learning for machine scheduling: Methodology, the state-of-the-art, and future directionscited by 8
  14. 2024Learning to Optimize for Mixed-Integer Non-linear Programming with Feasibility Guaranteescited by 8
  15. 2021The Graph Neural Networking Challenge: A Worldwide Competition for Education in AI/ML for Networkscited by 5
  16. 2022Fast Continuous and Integer L-shaped Heuristics Through Supervised Learningcited by 5
  17. 2021Self-Supervision is All You Need for Solving Rubik's Cubecited by 4
  18. 2021Computing Steiner Trees using Graph Neural Networkscited by 4
  19. 2021Learning for Robust Combinatorial Optimization: Algorithm and Applicationcited by 4
  20. 2023Two-Stage Learning For the Flexible Job Shop Scheduling Problemcited by 4
  21. 2023Learning to Branch in Combinatorial Optimization with Graph Pointer Networkscited by 4
  22. 2023Predict-Then-Optimize by Proxy: Learning Joint Models of Prediction and Optimizationcited by 4
  23. 2024A Unified Framework for Combinatorial Optimization Based on Graph Neural Networkscited by 4
  24. 2025PROPEL: Supervised and Reinforcement Learning for Large-Scale Supply Chain Planningcited by 4
  25. 2026Survey on Neural Routing Solverscited by 4
  26. 2021Optimal Solving of Constrained Path-Planning Problems with Graph Convolutional Networks and Optimized Tree Searchcited by 3
  27. 2022End-to-End Pareto Set Prediction with Graph Neural Networks for Multi-objective Facility Locationcited by 3
  28. 2023Revisit the Algorithm Selection Problem for TSP with Spatial Information Enhanced Graph Neural Networkscited by 3
  29. 2023D2Match: Leveraging Deep Learning and Degeneracy for Subgraph Matchingcited by 3
  30. 2024Learning Joint Models of Prediction and Optimizationcited by 3
  31. 2022Understanding Curriculum Learning in Policy Optimization for Online Combinatorial Optimizationcited by 2
  32. 2022Actively Learning Costly Reward Functions for Reinforcement Learningcited by 2
  33. 2023VN-Solver: Vision-based Neural Solver for Combinatorial Optimization over Graphscited by 2
  34. 2024Multi-graph Graph Matching for Coronary Artery Semantic Labelingcited by 2
  35. 2021DeepGANTT: A Scalable Deep Learning Scheduler for Backscatter Networkscited by 1
  36. 2022Pareto Frontier Approximation Network (PA-Net) to Solve Bi-objective TSPcited by 1
  37. 2022Solving Disjunctive Temporal Networks with Uncertainty under Restricted Time-Based Controllability using Tree Search and Graph Neural Networkscited by 1
  38. 2022An AlphaZero-Inspired Approach to Solving Search Problemscited by 1

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