在图数据库上直接训练GNN，减少内存占用并提升效率。
Graph Neural Networks on Graph Databases
- 利用图数据库查询引擎采样，仅加载必要数据到内存
- 单机和分布式训练均显著降低资源消耗
- 为GNN扩展提供新路径，也为图数据库开辟新应用
在大规模数据集上训练图神经网络长期面临挑战。传统方法包括将全图高效存储于内存、设计参数高效且基于采样的模型，以及在分布式环境下进行图划分。与此同时，具备原生图存储与查询引擎的图数据库已发展成熟，能高效支持图分析任务。本文展示如何直接在图数据库上训练GNN，通过查询引擎仅提取最小数据量至内存并完成采样。实验表明，该方法在单机与分布式训练中均具备资源优势。本方法为GNN的扩展提供了新思路，也拓展了图数据库的应用场景。
原文摘要 · Abstract (English)
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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