mlsanityData to 7 Oct 2026

Neural Graph Databases

arXiv 2209.09732 ↗First posted 20 Sep 2022cs.LG cs.DBcited by 11

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Abstract (arXiv)

Graph databases (GDBs) enable processing and analysis of unstructured, complex, rich, and usually vast graph datasets. Despite the large significance of GDBs in both academia and industry, little effort has been made into integrating them with the predictive power of graph neural networks (GNNs). In this work, we show how to seamlessly combine nearly any GNN model with the computational capabilities of GDBs. For this, we observe that the majority of these systems are based on, or support, a graph data model called the Labeled Property Graph (LPG), where vertices and edges can have arbitrarily complex sets of labels and properties. We then develop LPG2vec, an encoder that transforms an arbitrary LPG dataset into a representation that can be directly used with a broad class of GNNs, including convolutional, attentional, message-passing, and even higher-order or spectral models. In our evaluation, we show that the rich information represented as LPG labels and properties is properly preserved by LPG2vec, and it increases the accuracy of predictions regardless of the targeted learning task or the used GNN model, by up to 34% compared to graphs with no LPG labels/properties. In general, LPG2vec enables combining predictive power of the most powerful GNNs with the full scope of information encoded in the LPG model, paving the way for neural graph databases, a class of systems where the vast complexity of maintained data will benefit from modern and future graph machine learning methods.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 95 of its 191 references that are in the map. First posted on arXiv 20 Sep 2022.

Builds on

95 of 191 references.

References · 95

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38 more
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50 most cited of 95 All 95 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 4; 2025: 3; 2026: 2.

11 papers.

  1. 2023Graph of Thoughts: Solving Elaborate Problems with Large Language Modelscited by 873
  2. 2024Demystifying Chains, Trees, and Graphs of Thoughtscited by 63
  3. 2023Neural Graph Reasoning: Complex Logical Query Answering Meets Graph Databasescited by 35
  4. 2025Top Ten Challenges Towards Agentic Neural Graph Databasescited by 13
  5. 2024Learning Federated Neural Graph Databases for Answering Complex Queries from Distributed Knowledge Graphscited by 4
  6. 2024Demystifying Higher-Order Graph Neural Networkscited by 4
  7. 2026NGDB-Zoo: Towards Efficient and Scalable Neural Graph Databases Trainingcited by 2
  8. 2026KGPFN: Unlocking the Potential of Knowledge Graph Foundation Model via In-Context Learningcited by 2
  9. 2024Graph Neural Networks on Graph Databasescited by 0
  10. 2025Improving Graph Embeddings in Machine Learning Using Knowledge Completion with Validation in a Case Study on COVID-19 Spreadcited by 0
  11. 2025Bi-View Embedding Fusion: A Hybrid Learning Approach for Knowledge Graph's Nodes Classification Addressing Problems with Limited Datacited by 0