mlsanityData to 14 Oct 2026

Graph Neural Networks for Databases: A Survey

arXiv 2502.12908 ↗First posted 18 Feb 2025cs.DB cs.AIcited by 6

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Abstract (arXiv)

Graph neural networks (GNNs) are powerful deep learning models for graph-structured data, demonstrating remarkable success across diverse domains. Recently, the database (DB) community has increasingly recognized the potentiality of GNNs, prompting a surge of researches focusing on improving database systems through GNN-based approaches. However, despite notable advances, There is a lack of a comprehensive review and understanding of how GNNs could improve DB systems. Therefore, this survey aims to bridge this gap by providing a structured and in-depth overview of GNNs for DB systems. Specifically, we propose a new taxonomy that classifies existing methods into two key categories: (1) Relational Databases, which includes tasks like performance prediction, query optimization, and text-to-SQL, and (2) Graph Databases, addressing challenges like efficient graph query processing and graph similarity computation. We systematically review key methods in each category, highlighting their contributions and practical implications. Finally, we suggest promising avenues for integrating GNNs into Database systems.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 36 of its 69 references that are in the map. First posted on arXiv 18 Feb 2025.

Builds on

36 of 69 references.

References · 36

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2017Graph Attention Networkscited by 8,923
  3. 2017Modeling Relational Data with Graph Convolutional Networkscited by 1,694
  4. 2020Can Graph Neural Networks Count Substructures?cited by 236
  5. 2019Graph Matching Networks for Learning the Similarity of Graph Structured Objectscited by 205
  6. 2018Learning Scheduling Algorithms for Data Processing Clusterscited by 134
  7. 2021LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relationscited by 119
  8. 2023Graphix-T5: Mixing Pre-Trained Transformers with Graph-Aware Layers for Text-to-SQL Parsingcited by 93
  9. 2018SimGNN: A Neural Network Approach to Fast Graph Similarity Computationcited by 90
  10. 2019Neo: A Learned Query Optimizercited by 80
  11. 2019Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsingcited by 74
  12. 2020Bao: Learning to Steer Query Optimizerscited by 62
24 more
  1. 2020Neural Subgraph Matchingcited by 47
  2. 2018Learning-based Efficient Graph Similarity Computation via Multi-Scale Convolutional Set Matchingcited by 46
  3. 2019Global Reasoning over Database Structures for Text-to-SQL Parsingcited by 43
  4. 2021SADGA: Structure-Aware Dual Graph Aggregation Network for Text-to-SQLcited by 43
  5. 2021ShadowGNN: Graph Projection Neural Network for Text-to-SQL Parsercited by 35
  6. 2021GREED: A Neural Framework for Learning Graph Distance Functionscited by 32
  7. 2020Multilevel Graph Matching Networks for Deep Graph Similarity Learningcited by 29
  8. 2019Neural Subgraph Isomorphism Countingcited by 27
  9. 2024HAIChart: Human and AI Paired Visualization Systemcited by 26
  10. 2024Efficient Graph Similarity Computation with Alignment Regularizationcited by 26
  11. 2020Combinatorial Learning of Graph Edit Distance via Dynamic Embeddingcited by 25
  12. 2022Semantic Enhanced Text-to-SQL Parsing via Iteratively Learning Schema Linking Graphcited by 22
  13. 2020Tracking Interaction States for Multi-Turn Text-to-SQL Semantic Parsingcited by 18
  14. 2023VerifAI: Verified Generative AIcited by 16
  15. 2023Learning to Count Isomorphisms with Graph Neural Networkscited by 13
  16. 2023MATA*: Combining Learnable Node Matching with A* Algorithm for Approximate Graph Edit Distance Computationcited by 10
  17. 2023Efficient Exact Subgraph Matching via GNN-based Path Dominance Embedding (Technical Report)cited by 9
  18. 2022Reinforcement Learning Based Query Vertex Ordering Model for Subgraph Matchingcited by 6
  19. 2023DeSCo: Towards Generalizable and Scalable Deep Subgraph Countingcited by 4
  20. 2024AutoCE: An Accurate and Efficient Model Advisor for Learned Cardinality Estimationcited by 4
  21. 2023Cardinality Estimation over Knowledge Graphs with Embeddings and Graph Neural Networkscited by 3
  22. 2024Stage: Query Execution Time Prediction in Amazon Redshiftcited by 3
  23. 2024Cardinality Estimation on Hyper-relational Knowledge Graphscited by 3
  24. 2024Blueprinting the Cloud: Unifying and Automatically Optimizing Cloud Data Infrastructures with BRAD -- Extended Versioncited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 4.

6 papers.

  1. 2025Reward-SQL: Boosting Text-to-SQL via Stepwise Execution-Aware Reasoning and Process-Supervised Rewardscited by 27
  2. 2026EvolveRouter: Co-Evolving Routing and Prompt for Multi-Agent Question Answeringcited by 15
  3. 2026Graph is a Substrate Across Data Modalitiescited by 14
  4. 2025Relational Database Distillation: From Structured Tables to Condensed Graph Datacited by 1
  5. 2026Generalizing GNNs with Tokenized Mixture of Expertscited by 1
  6. 2026On the Safety of Graph Representation Learningcited by 0