An increasing number of machine learning tasks require dealing with large
graph datasets, which capture rich and complex relationship among potentially
billions of elements. Graph Neural Network (GNN) becomes an effective way to
address the graph learning problem by converting the graph data into a low
dimensional space while keeping both the structural and property information to
the maximum extent and constructing a neural network for training and
referencing. However, it is challenging to provide an efficient graph storage
and computation capabilities to facilitate GNN training and enable development
of new GNN algorithms. In this paper, we present a comprehensive graph neural
network system, namely AliGraph, which consists of distributed graph storage,
optimized sampling operators and runtime to efficiently support not only
existing popular GNNs but also a series of in-house developed ones for
different scenarios. The system is currently deployed at Alibaba to support a
variety of business scenarios, including product recommendation and
personalized search at Alibaba's E-Commerce platform. By conducting extensive
experiments on a real-world dataset with 492.90 million vertices, 6.82 billion
edges and rich attributes, AliGraph performs an order of magnitude faster in
terms of graph building (5 minutes vs hours reported from the state-of-the-art
PowerGraph platform). At training, AliGraph runs 40%-50% faster with the novel
caching strategy and demonstrates around 12 times speed up with the improved
runtime. In addition, our in-house developed GNN models all showcase their
statistically significant superiorities in terms of both effectiveness and
efficiency (e.g., 4.12%-17.19% lift by F1 scores).

通过构建一个分布式图存储的 AliGraph 系统，本论文针对大型图数据集上的机器学习问题，实现了高效的图神经网络训练和开发，并在阿里巴巴的电商平台上实际应用于产品推荐和个性化搜索等业务场景中，提高了训练效率和模型效果。