# Graph Neural Networks on Graph Databases

arXiv: https://arxiv.org/abs/2411.11375 · HTML: https://mlsanity.com/paper/2411.11375/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 46 references that are in the map.
- First posted on arXiv 18 Nov 2024.

## Builds on (most cited first, 25 of 25)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Open Graph Benchmark: Datasets for Machine Learning on Graphs](https://mlsanity.com/paper/2005.00687/) (2020)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [From Local to Global: A Graph RAG Approach to Query-Focused Summarization](https://mlsanity.com/paper/2404.16130/) (2024)
- [Heterogeneous Graph Attention Network](https://mlsanity.com/paper/1903.07293/) (2019)
- [FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling](https://mlsanity.com/paper/1801.10247/) (2018)
- [MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding](https://mlsanity.com/paper/2002.01680/) (2020)
- [Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks](https://mlsanity.com/paper/1911.07323/) (2019)
- [DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs](https://mlsanity.com/paper/2010.05337/) (2020)
- [GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoning](https://mlsanity.com/paper/2405.20139/) (2024)
- [Relational Deep Learning: Graph Representation Learning on Relational Databases](https://mlsanity.com/paper/2312.04615/) (2023)
- [Sampling methods for efficient training of graph convolutional networks: A survey](https://mlsanity.com/paper/2103.05872/) (2021)
- [Neural Graph Reasoning: Complex Logical Query Answering Meets Graph Databases](https://mlsanity.com/paper/2303.14617/) (2023)
- [Foundations of Modern Query Languages for Graph Databases](https://mlsanity.com/paper/1610.06264/) (2016)
- [TF-GNN: Graph Neural Networks in TensorFlow](https://mlsanity.com/paper/2207.03522/) (2022)
- [Learning Models over Relational Data using Sparse Tensors and Functional Dependencies](https://mlsanity.com/paper/1703.04780/) (2017)
- [Neural Graph Databases](https://mlsanity.com/paper/2209.09732/) (2022)
- [Graph Pattern Matching in GQL and SQL/PGQ](https://mlsanity.com/paper/2112.06217/) (2021)
- [The Relational Data Borg is Learning](https://mlsanity.com/paper/2008.07864/) (2020)
- [The Graph Database Interface: Scaling Online Transactional and Analytical Graph Workloads to Hundreds of Thousands of Cores](https://mlsanity.com/paper/2305.11162/) (2023)
- [Formal Semantics of the Language Cypher](https://mlsanity.com/paper/1802.09984/) (2018)
- [Communication-Free Distributed GNN Training with Vertex Cut](https://mlsanity.com/paper/2308.03209/) (2023)
- [G-Tran: Making Distributed Graph Transactions Fast](https://mlsanity.com/paper/2105.04449/) (2021)

## Built on by (most cited first, 1 of 1)

- [Towards Training and Inference of Graph Neural Networks in Graph Databases](https://mlsanity.com/paper/2610.19142/) (2026)

## Abstract (arXiv)

Training graph neural networks on large datasets has long been a challenge. Traditional approaches include efficiently representing the whole graph in-memory, designing parameter efficient and sampling-based models, and graph partitioning in a distributed setup. Separately, graph databases with native graph storage and query engines have been developed, which enable time and resource efficient graph analytics workloads. We show how to directly train a GNN on a graph DB, by retrieving minimal data into memory and sampling using the query engine. Our experiments show resource advantages for single-machine and distributed training. Our approach opens up a new way of scaling GNNs as well as a new application area for graph DBs.