# Towards Training and Inference of Graph Neural Networks in Graph Databases

arXiv: https://arxiv.org/abs/2610.19142 · HTML: https://mlsanity.com/paper/2610.19142/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 33 references that are in the map.
- First posted on arXiv 13 Oct 2026.

## Builds on (most cited first, 12 of 12)

- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [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)
- [Revisiting Semi-Supervised Learning with Graph Embeddings](https://mlsanity.com/paper/1603.08861/) (2016)
- [FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling](https://mlsanity.com/paper/1801.10247/) (2018)
- [AliGraph: A Comprehensive Graph Neural Network Platform](https://mlsanity.com/paper/1902.08730/) (2019)
- [SmartSAGE: Training Large-scale Graph Neural Networks using In-Storage Processing Architectures](https://mlsanity.com/paper/2205.04711/) (2022)
- [Formal Semantics of the Language Cypher](https://mlsanity.com/paper/1802.09984/) (2018)
- [Ripple: Scalable Incremental GNN Inferencing on Large Streaming Graphs](https://mlsanity.com/paper/2505.12112/) (2025)
- [STAG: Enabling Low Latency and Low Staleness of GNN-based Services with Dynamic Graphs](https://mlsanity.com/paper/2309.15875/) (2023)
- [Graph Neural Networks on Graph Databases](https://mlsanity.com/paper/2411.11375/) (2024)

## Abstract (arXiv)

Graph Neural Networks on large graphs commonly rely on in-memory graph storage with ad hoc data processing pipelines for both training and inference. Graph databases provide scalable storage and efficient traversal for graphs that can evolve over time, but their applicability in GNN training and inference are rarely studied. We show that graph DBs can be used in GNN training and inference directly, executing sampling, feature retrieval, and even the full forward pass. We show different GNN training pipelines can perform on-the-fly sampling and retrieval in graph DBs using Cypher. We further provide optimisations that speed up the training process by ~2.5x while maintaining the same accuracy. We illustrate that fully in-DB inference is possible, independent of how the GNNs are trained. Our inference setup can be >10x faster than cold loading the graph into RAM with no extra memory overhead on real-world graphs. Our work lays the foundation for in-graph-DB GNN training and inference.