mlsanityData to 7 Oct 2026

Neural Subgraph Matching

arXiv 2007.03092 ↗First posted 6 Jul 2020cs.LG stat.MLcited by 47

Not on the map. Why?

Abstract (arXiv)

Subgraph matching is the problem of determining the presence and location(s) of a given query graph in a large target graph. Despite being an NP-complete problem, the subgraph matching problem is crucial in domains ranging from network science and database systems to biochemistry and cognitive science. However, existing techniques based on combinatorial matching and integer programming cannot handle matching problems with both large target and query graphs. Here we propose NeuroMatch, an accurate, efficient, and robust neural approach to subgraph matching. NeuroMatch decomposes query and target graphs into small subgraphs and embeds them using graph neural networks. Trained to capture geometric constraints corresponding to subgraph relations, NeuroMatch then efficiently performs subgraph matching directly in the embedding space. Experiments demonstrate NeuroMatch is 100x faster than existing combinatorial approaches and 18% more accurate than existing approximate subgraph matching methods.

Key facts. Cited by 47 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Jul 2020.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 11; 2023: 7; 2024: 7; 2025: 14; 2026: 5.

47 papers.

Sort:

By area:

  1. 2021GREED: A Neural Framework for Learning Graph Distance Functionscited by 31
  2. 2022Algorithm and System Co-design for Efficient Subgraph-based Graph Representation Learningcited by 28
  3. 2021Logic Embeddings for Complex Query Answeringcited by 17
  4. 2021Graph Convolutional Networks with Dual Message Passing for Subgraph Isomorphism Counting and Matchingcited by 11
  5. 2024SG-PGM: Partial Graph Matching Network with Semantic Geometric Fusion for 3D Scene Graph Alignment and Its Downstream Taskscited by 10
  6. 2024Graph Edit Distance with General Costs Using Neural Set Divergencecited by 10
  7. 2022Maximum Common Subgraph Guided Graph Retrieval: Late and Early Interaction Networkscited by 8
  8. 2023SUREL+: Moving from Walks to Sets for Scalable Subgraph-based Graph Representation Learningcited by 8
  9. 2023RAHNet: Retrieval Augmented Hybrid Network for Long-tailed Graph Classificationcited by 7
  10. 2025Towards A Universal Graph Structural Encodercited by 7
  11. 2025Graph-Reward-SQL: Execution-Free Reinforcement Learning for Text-to-SQL via Graph Matching and Stepwise Rewardcited by 7
  12. 2022Reinforcement Learning Based Query Vertex Ordering Model for Subgraph Matchingcited by 6
35 more
  1. 2025Graph Neural Networks for Databases: A Surveycited by 6
  2. 2022DeepGAR: Deep Graph Learning for Analogical Reasoningcited by 5
  3. 2024Representation Learning for Frequent Subgraph Miningcited by 5
  4. 2022Schema-Guided Event Graph Completioncited by 4
  5. 2023GNNPipe: Scaling Deep GNN Training with Pipelined Model Parallelismcited by 4
  6. 2022FLEX: Feature-Logic Embedding Framework for CompleX Knowledge Graph Reasoningcited by 3
  7. 2022Towards Accurate Subgraph Similarity Computation via Neural Graph Pruningcited by 3
  8. 2023D2Match: Leveraging Deep Learning and Degeneracy for Subgraph Matchingcited by 3
  9. 2025Neural Graduated Assignment for Maximum Common Edge Subgraphscited by 3
  10. 2022Learning Universe Model for Partial Matching Networks over Multiple Graphscited by 2
  11. 2025SCENIR: Visual Semantic Clarity through Unsupervised Scene Graph Retrievalcited by 2
  12. 2025Iteratively Refined Early Interaction Alignment for Subgraph Matching based Graph Retrievalcited by 2
  13. 2022Interactive Visual Pattern Search on Graph Data via Graph Representation Learningcited by 1
  14. 2022Detecting Small Query Graphs in A Large Graph via Neural Subgraph Searchcited by 1
  15. 2024PharmacoMatch: Efficient 3D Pharmacophore Screening via Neural Subgraph Matchingcited by 1
  16. 2025Graph Alignment for Benchmarking Graph Neural Networks and Learning Positional Encodingscited by 1
  17. 2025Functional Matching of Logic Subgraphs: Beyond Structural Isomorphismcited by 1
  18. 2025Improving Subgraph Matching by Combining Algorithms and Graph Neural Networkscited by 1
  19. 2026End-to-End Subgraph Detection with GraphDETRcited by 1
  20. 2022A Survey on Machine Learning Solutions for Graph Pattern Extractioncited by 0
  21. 2023Causal schema induction for knowledge discoverycited by 0
  22. 2023xNeuSM: Explainable Neural Subgraph Matching with Graph Learnable Multi-hop Attention Networkscited by 0
  23. 2023CodeScholar: Growing Idiomatic Code Examplescited by 0
  24. 2024Solving the Tree Containment Problem Using Graph Neural Networkscited by 0
  25. 2024Towards Subgraph Isomorphism Counting with Graph Kernelscited by 0
  26. 2024Graph Similarity Computation via Interpretable Neural Node Alignmentcited by 0
  27. 2025Target Circuit Matching in Large-Scale Netlists using GNN-Based Region Predictioncited by 0
  28. 2025Hierarchy-Aware Neural Subgraph Matching with Enhanced Similarity Measurecited by 0
  29. 2025Contextual Tokenization for Graph Inverted Indicescited by 0
  30. 2025Charting the Design Space of Neural Graph Representations for Subgraph Matchingcited by 0
  31. 2025Neural Graph Navigation for Intelligent Subgraph Matchingcited by 0
  32. 2026Approximate Subgraph Matching with Neural Graph Representations and Reinforcement Learningcited by 0
  33. 2026NGM-RAG: Neural Graph Matching based Retrieval-Augmented Generationcited by 0
  34. 2026Controllable GNN Explanations via Multi-Metric Preference Selectioncited by 0
  35. 2026Equivariant Neural Primal-Dual Assignment for Maximum Common Edge Subgraphscited by 0