mlsanityData to 14 Oct 2026

Graph Neural Networks on Graph Databases

arXiv 2411.11375 ↗First posted 18 Nov 2024cs.LG cs.DBcited by 1

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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.

Key facts. Cited by 1 paper 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

25 of 46 references.

References · 25

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2017Inductive Representation Learning on Large Graphscited by 6,320
  3. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,434
  4. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,163
  5. 2016node2vec: Scalable Feature Learning for Networkscited by 1,918
  6. 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarizationcited by 1,453
  7. 2019Heterogeneous Graph Attention Networkcited by 734
  8. 2018FastGCN: Fast Learning with Graph Convolutional Networks via Importance Samplingcited by 584
  9. 2020MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embeddingcited by 304
  10. 2019Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networkscited by 177
  11. 2020DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphscited by 139
  12. 2024GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoningcited by 128
13 more
  1. 2023Relational Deep Learning: Graph Representation Learning on Relational Databasescited by 56
  2. 2021Sampling methods for efficient training of graph convolutional networks: A surveycited by 40
  3. 2023Neural Graph Reasoning: Complex Logical Query Answering Meets Graph Databasescited by 35
  4. 2016Foundations of Modern Query Languages for Graph Databasescited by 22
  5. 2022TF-GNN: Graph Neural Networks in TensorFlowcited by 21
  6. 2017Learning Models over Relational Data using Sparse Tensors and Functional Dependenciescited by 12
  7. 2022Neural Graph Databasescited by 11
  8. 2021Graph Pattern Matching in GQL and SQL/PGQcited by 10
  9. 2020The Relational Data Borg is Learningcited by 6
  10. 2023The Graph Database Interface: Scaling Online Transactional and Analytical Graph Workloads to Hundreds of Thousands of Corescited by 5
  11. 2018Formal Semantics of the Language Cyphercited by 3
  12. 2023Communication-Free Distributed GNN Training with Vertex Cutcited by 2
  13. 2021G-Tran: Making Distributed Graph Transactions Fastcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Towards Training and Inference of Graph Neural Networks in Graph Databasescited by 0