Survey on Graph Neural Network Acceleration: An Algorithmic Perspective

• URL: http://arxiv.org/abs/2202.04822v1
• Date: Thu, 10 Feb 2022 04:01:40 GMT
• Title: Survey on Graph Neural Network Acceleration: An Algorithmic Perspective
• Authors: Xin Liu, Mingyu Yan, Lei Deng, Guoqi Li, Xiaochun Ye, Dongrui Fan, Shirui Pan, Yuan Xie
• Abstract summary: Graph neural networks (GNNs) have been a hot spot of recent research and are widely utilized in diverse applications. We provide a comprehensive survey on acceleration methods for GNNs from an algorithmic perspective.
• Score: 42.88720757069427
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph neural networks (GNNs) have been a hot spot of recent research and are widely utilized in diverse applications. However, with the use of huger data and deeper models, an urgent demand is unsurprisingly made to accelerate GNNs for more efficient execution. In this paper, we provide a comprehensive survey on acceleration methods for GNNs from an algorithmic perspective. We first present a new taxonomy to classify existing acceleration methods into five categories. Based on the classification, we systematically discuss these methods and highlight their correlations. Next, we provide comparisons from aspects of the efficiency and characteristics of these methods. Finally, we suggest some promising prospects for future research.

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