mlsanityData to 14 Oct 2026

Deep Graph Matching Consensus

arXiv 2001.09621 ↗First posted 27 Jan 2020cs.LG stat.MLcited by 85

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Abstract (arXiv)

This work presents a two-stage neural architecture for learning and refining structural correspondences between graphs. First, we use localized node embeddings computed by a graph neural network to obtain an initial ranking of soft correspondences between nodes. Secondly, we employ synchronous message passing networks to iteratively re-rank the soft correspondences to reach a matching consensus in local neighborhoods between graphs. We show, theoretically and empirically, that our message passing scheme computes a well-founded measure of consensus for corresponding neighborhoods, which is then used to guide the iterative re-ranking process. Our purely local and sparsity-aware architecture scales well to large, real-world inputs while still being able to recover global correspondences consistently. We demonstrate the practical effectiveness of our method on real-world tasks from the fields of computer vision and entity alignment between knowledge graphs, on which we improve upon the current state-of-the-art. Our source code is available under https://github.com/rusty1s/ deep-graph-matching-consensus.

Key facts. Cited by 85 papers in the map (data through 14 Oct 2026). First posted on arXiv 27 Jan 2020.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 27; 2022: 24; 2023: 14; 2024: 11; 2025: 8; 2026: 1.

85 papers.

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  1. 2021Combinatorial optimization and reasoning with graph neural networkscited by 268
  2. 2021Weisfeiler and Leman go Machine Learning: The Story so farcited by 122
  3. 2021Learning to Match Features with Seeded Graph Matching Networkcited by 92
  4. 2021A comprehensive survey on point cloud registrationcited by 84
  5. 2022ClusterGNN: Cluster-based Coarse-to-Fine Graph Neural Network for Efficient Feature Matchingcited by 62
  6. 2025PyG 2.0: Scalable Learning on Real World Graphscited by 52
  7. 2021CombOptNet: Fit the Right NP-Hard Problem by Learning Integer Programming Constraintscited by 49
  8. 2022MIP-GNN: A Data-Driven Framework for Guiding Combinatorial Solverscited by 49
  9. 2021Boost then Convolve: Gradient Boosting Meets Graph Neural Networkscited by 44
  10. 2021Convolutional Hough Matching Networkscited by 43
  11. 2023Equivariant Deep Weight Space Alignmentcited by 35
  12. 2022SelfKG: Self-Supervised Entity Alignment in Knowledge Graphscited by 32
38 more
  1. 2022Graph Matching with Bi-level Noisy Correspondencecited by 30
  2. 2021RAGA: Relation-aware Graph Attention Networks for Global Entity Alignmentcited by 29
  3. 2023Scalable Optimal Transport Methods in Machine Learning: A Contemporary Surveycited by 27
  4. 2021From Alignment to Assignment: Frustratingly Simple Unsupervised Entity Alignmentcited by 26
  5. 2024Towards Graph Contrastive Learning: A Survey and Beyondcited by 26
  6. 2022Exponentially Improving the Complexity of Simulating the Weisfeiler-Lehman Test with Graph Neural Networkscited by 21
  7. 2021Knowing the No-match: Entity Alignment with Dangling Casescited by 20
  8. 2022ClusterEA: Scalable Entity Alignment with Stochastic Training and Normalized Mini-batch Similaritiescited by 20
  9. 2021The Power of the Weisfeiler-Leman Algorithm for Machine Learning with Graphscited by 19
  10. 2023Unsupervised Entity Alignment for Temporal Knowledge Graphscited by 19
  11. 2021Deep Graph Matching under Quadratic Constraintcited by 18
  12. 2022LightEA: A Scalable, Robust, and Interpretable Entity Alignment Framework via Three-view Label Propagationcited by 18
  13. 2021Fusion Moves for Graph Matchingcited by 16
  14. 2022A Unified Framework for Rank-based Evaluation Metrics for Link Prediction in Knowledge Graphscited by 15
  15. 2023Robust Attributed Graph Alignment via Joint Structure Learning and Optimal Transportcited by 15
  16. 2023Graph Mixup with Soft Alignmentscited by 15
  17. 2023Factor Graph Neural Networkscited by 15
  18. 2022SRFeat: Learning Locally Accurate and Globally Consistent Non-Rigid Shape Correspondencecited by 13
  19. 2021Convolutional Hough Matching Networks for Robust and Efficient Visual Correspondencecited by 12
  20. 2021Stochastic Iterative Graph Matchingcited by 10
  21. 2023MATA*: Combining Learnable Node Matching with A* Algorithm for Approximate Graph Edit Distance Computationcited by 10
  22. 2024SG-PGM: Partial Graph Matching Network with Semantic Geometric Fusion for 3D Scene Graph Alignment and Its Downstream Taskscited by 10
  23. 2021Are Negative Samples Necessary in Entity Alignment? An Approach with High Performance, Scalability and Robustnesscited by 9
  24. 2021Joint Graph Learning and Matching for Semantic Feature Correspondencecited by 8
  25. 2022Maximum Common Subgraph Guided Graph Retrieval: Late and Early Interaction Networkscited by 8
  26. 2022Interactive Contrastive Learning for Self-supervised Entity Alignmentcited by 7
  27. 2022Ensemble Semi-supervised Entity Alignment via Cycle-teachingcited by 7
  28. 2024Synchronous Diffusion for Unsupervised Smooth Non-Rigid 3D Shape Matchingcited by 7
  29. 2022Efficient Linear Attention for Fast and Accurate Keypoint Matchingcited by 6
  30. 2023Generalizable Local Feature Pre-training for Deformable Shape Analysiscited by 6
  31. 2024Ensemble Quadratic Assignment Network for Graph Matchingcited by 6
  32. 2021COTReg:Coupled Optimal Transport based Point Cloud Registrationcited by 5
  33. 2022PAGE: Prototype-Based Model-Level Explanations for Graph Neural Networkscited by 5
  34. 2024Representation Learning for Frequent Subgraph Miningcited by 5
  35. 2024A Unified Framework for Combinatorial Optimization Based on Graph Neural Networkscited by 5
  36. 2021Towards Entity Alignment in the Open World: An Unsupervised Approachcited by 4
  37. 2022Dangling-Aware Entity Alignment with Mixed High-Order Proximitiescited by 4
  38. 2025Towards graph neural networks for provably solving convex optimization problemscited by 4

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