Towards Training and Inference of Graph Neural Networks in Graph Databases
We show that graph DBs can be used in GNN training and inference directly, executing sampling, feature retrieval, and even the full forward pass.
ProofPaper ↗
Key points
- 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 illustrate that fully in-DB inference is possible, independent of how the GNNs are trained.
- Our work lays the foundation for in-graph-DB GNN training and inference.
Sources (1)
- [1]Towards Training and Inference of Graph Neural Networks in Graph DatabasesarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 08:09 PM
We show that graph DBs can be used in GNN training and inference directly, executing sampling, feature retrieval, and even the full forward pass.
Graph Neural Networks on large graphs commonly rely on in-memory graph storage with ad hoc data processing pipelines for both training and inference.
Extractive summary: sentences quoted from the sources.
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