AliGraph: A Comprehensive Graph Neural Network Platform
AliGraph: A Comprehensive Graph Neural Network Platform
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DOI：
10.14778/3352063.3352127
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发表时间：
2019-02
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通讯作者：
Rong Zhu;Kun Zhao;Hongxia Yang;Wei Lin;Chang Zhou;Baole Ai;Yong Li;Jingren Zhou
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文献类型：
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作者：
Rong Zhu;Kun Zhao;Hongxia Yang;Wei Lin;Chang Zhou;Baole Ai;Yong Li;Jingren Zhou
越来越多的机器学习任务需要处理大型图形数据集，这些数据集可以捕获潜在数十亿元素之间丰富而复杂的关系。图神经网络（Graph Neural Network， GNN）通过将图数据转换为低维空间，最大限度地保留图数据的结构信息和属性信息，构建神经网络进行训练和引用，成为解决图学习问题的有效方法。然而，如何提供高效的图存储和计算能力来促进GNN训练和开发新的GNN算法是一个挑战。在本文中，我们提出了一个综合的图神经网络系统，即AliGraph，它由分布式图存储、优化的采样算子和运行时组成，不仅有效地支持现有的流行gnn，而且还支持一系列针对不同场景的自主开发的gnn。该系统目前部署在阿里巴巴，以支持各种业务场景，包括阿里巴巴电子商务平台的产品推荐和个性化搜索。通过在一个拥有4.929亿个顶点、68.2亿个边和丰富属性的真实数据集上进行广泛的实验，Ali- Graph在图构建方面的执行速度快了一个数量级（5分钟比最先进的PowerGraph平台报告的小时）。在训练中，使用新的缓存策略，AliGraph的运行速度提高了40%-50%，并且通过改进的运行时，速度提高了约12倍。此外，我们内部开发的GNN模型在有效性和效率方面都显示出统计学上显著的优势（例如，F1分数提升4.12% 17.19%）。
An increasing number of machine learning tasks require dealing with large graph datasets, which capture rich and complex relation- ship 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, Ali- Graph 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).