AliGraph: A Comprehensive Graph Neural Network Platform

By Rong Zhu, Kun Zhao, Hongxia Yang, Wei Lin, Chang Zhou, Baole Ai, Yong Li, Jingren Zhou. Proceedings of the VLDB Endowment, 2019

469 citations recorded by the source corpus.

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).

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems

View Paper: https://www.semanticscholar.org/paper/70f7cd54b5918aed60c3d8c3a8e4aa7e8d634c41

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Citations in the corpus

Reliable Data Distillation on Graph Convolutional Network (2020, background): “Currently, the neural networks on the graph have attracted much attention due to its great performances [16, 53].”
A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions (2021, methodology): “In addition, a few open-source tools have been released that can accelerate the research and development of GNN-based recommendation, such as PyG [48], DGL [174], and AliGraph [252].”
AntMan: Dynamic Scaling on GPU Clusters for Deep Learning (2020, methodology): “Training a DL model often requires a mixture of computations, some of which can hardly be parallelized using GPU, such as graph sampling in graph neural network [21, 54], feature extraction in advertisement [15, 23], data augmentation in computer vision [56], etc.”
DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs (2020, background): “Multiple GNN frameworks [15]–[17] built by industry adopt distributed mini-batch training.”
ETA Prediction with Graph Neural Networks in Google Maps (2021, background): “GNNs have also been deployed for product recommendation at Amazon [12], E-commerce applications at Alibaba [32], engagement forecasting and friend ranking in Snapchat [22, 24], and most relevantly, they are powering traffic predictions within Baidu Maps [6].”
Deep Learning on Graphs: A Survey (2018, unknown): “com/dmlc/dgl PyTorch Improved efficiency, unified operations, scalability AliGraph [166] https://github.”
AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing (2019, background): “…number of real-world applications [8], including electric grid cascading failure analysis [32], prediction of chemical reactivity [33], prediction of synthesized material properties [34], polypharmacy side-effect modeling [35], accurate advertisement in E-commerce [36], and cybersecurity [37].”
Transformers in Time-Series Analysis: A Tutorial (2022, methodology): “The Trafﬁc Transformer [67] has been built based on two existing networks, the ﬁrst being a Graph Neural Network [90, 91] followed by the Transformer as shown in Figure 15.”
HyGCN: A GCN Accelerator with Hybrid Architecture (2020, methodology): “The convolutional layers occupy the major execution time of GCNs through two primary execution phases: Aggregation and Combination [1, 16, 17].”
Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks. (2019, background): “While this is the consensus among different packages (Fey & Lenssen, 2019; Battaglia et al., 2018; Zhu et al., 2019; Alibaba, 2019), DGL differs in a number of places.”