Graph Neural Networks on Graph Databases

• URL: http://arxiv.org/abs/2411.11375v1
• Date: Mon, 18 Nov 2024 08:39:24 GMT
• Title: Graph Neural Networks on Graph Databases
• Authors: Dmytro Lopushanskyy, Borun Shi,
• Abstract summary: 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 approach opens up a new way of scaling GNNs as well as a new application area for graph DBs.
• Score: 0.0
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• Abstract: 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.

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