# Graph Neural Networks for Databases: A Survey

arXiv: https://arxiv.org/abs/2502.12908 · HTML: https://mlsanity.com/paper/2502.12908/

## 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 (most cited first, 30 of 36)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Can Graph Neural Networks Count Substructures?](https://mlsanity.com/paper/2002.04025/) (2020)
- [Graph Matching Networks for Learning the Similarity of Graph Structured Objects](https://mlsanity.com/paper/1904.12787/) (2019)
- [Learning Scheduling Algorithms for Data Processing Clusters](https://mlsanity.com/paper/1810.01963/) (2018)
- [LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relations](https://mlsanity.com/paper/2106.01093/) (2021)
- [Graphix-T5: Mixing Pre-Trained Transformers with Graph-Aware Layers for Text-to-SQL Parsing](https://mlsanity.com/paper/2301.07507/) (2023)
- [SimGNN: A Neural Network Approach to Fast Graph Similarity Computation](https://mlsanity.com/paper/1808.05689/) (2018)
- [Neo: A Learned Query Optimizer](https://mlsanity.com/paper/1904.03711/) (2019)
- [Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing](https://mlsanity.com/paper/1905.06241/) (2019)
- [Bao: Learning to Steer Query Optimizers](https://mlsanity.com/paper/2004.03814/) (2020)
- [Neural Subgraph Matching](https://mlsanity.com/paper/2007.03092/) (2020)
- [Learning-based Efficient Graph Similarity Computation via Multi-Scale Convolutional Set Matching](https://mlsanity.com/paper/1809.04440/) (2018)
- [Global Reasoning over Database Structures for Text-to-SQL Parsing](https://mlsanity.com/paper/1908.11214/) (2019)
- [SADGA: Structure-Aware Dual Graph Aggregation Network for Text-to-SQL](https://mlsanity.com/paper/2111.00653/) (2021)
- [ShadowGNN: Graph Projection Neural Network for Text-to-SQL Parser](https://mlsanity.com/paper/2104.04689/) (2021)
- [GREED: A Neural Framework for Learning Graph Distance Functions](https://mlsanity.com/paper/2112.13143/) (2021)
- [Multilevel Graph Matching Networks for Deep Graph Similarity Learning](https://mlsanity.com/paper/2007.04395/) (2020)
- [Neural Subgraph Isomorphism Counting](https://mlsanity.com/paper/1912.11589/) (2019)
- [HAIChart: Human and AI Paired Visualization System](https://mlsanity.com/paper/2406.11033/) (2024)
- [Efficient Graph Similarity Computation with Alignment Regularization](https://mlsanity.com/paper/2406.14929/) (2024)
- [Combinatorial Learning of Graph Edit Distance via Dynamic Embedding](https://mlsanity.com/paper/2011.15039/) (2020)
- [Semantic Enhanced Text-to-SQL Parsing via Iteratively Learning Schema Linking Graph](https://mlsanity.com/paper/2208.03903/) (2022)
- [Tracking Interaction States for Multi-Turn Text-to-SQL Semantic Parsing](https://mlsanity.com/paper/2012.04995/) (2020)
- [VerifAI: Verified Generative AI](https://mlsanity.com/paper/2307.02796/) (2023)
- [Learning to Count Isomorphisms with Graph Neural Networks](https://mlsanity.com/paper/2302.03266/) (2023)
- [MATA*: Combining Learnable Node Matching with A* Algorithm for Approximate Graph Edit Distance Computation](https://mlsanity.com/paper/2311.02356/) (2023)
- [Efficient Exact Subgraph Matching via GNN-based Path Dominance Embedding (Technical Report)](https://mlsanity.com/paper/2309.15641/) (2023)
- [Reinforcement Learning Based Query Vertex Ordering Model for Subgraph Matching](https://mlsanity.com/paper/2201.11251/) (2022)

## Built on by (most cited first, 6 of 6)

- [Reward-SQL: Boosting Text-to-SQL via Stepwise Execution-Aware Reasoning and Process-Supervised Rewards](https://mlsanity.com/paper/2505.04671/) (2025)
- [EvolveRouter: Co-Evolving Routing and Prompt for Multi-Agent Question Answering](https://mlsanity.com/paper/2604.05149/) (2026)
- [Graph is a Substrate Across Data Modalities](https://mlsanity.com/paper/2601.22384/) (2026)
- [Relational Database Distillation: From Structured Tables to Condensed Graph Data](https://mlsanity.com/paper/2510.06980/) (2025)
- [Generalizing GNNs with Tokenized Mixture of Experts](https://mlsanity.com/paper/2602.09258/) (2026)
- [On the Safety of Graph Representation Learning](https://mlsanity.com/paper/2605.06576/) (2026)

## 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.