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Article overview
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AliGraph: A Comprehensive Graph Neural Network Platform | Rong Zhu
; Kun Zhao
; Hongxia Yang
; Wei Lin
; Chang Zhou
; Baole Ai
; Yong Li
; Jingren Zhou
; | Date: |
23 Feb 2019 | Abstract: | 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). | Source: | arXiv, 1902.8730 | Services: | Forum | Review | PDF | Favorites |
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