Survey on Graph Neural Network Acceleration: An Algorithmic Perspective
Xin Liu, Mingyu Yan, Lei Deng, Guoqi Li, Xiaochun Ye, Dongrui Fan, Shirui Pan, Yuan Xie
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.
BibTeX
@inproceedings{ijcai2022p772,
title = {Survey on Graph Neural Network Acceleration: An Algorithmic Perspective},
author = {Liu, Xin and Yan, Mingyu and Deng, Lei and Li, Guoqi and Ye, Xiaochun and Fan, Dongrui and Pan, Shirui and Xie, Yuan},
booktitle = {Proceedings of the Thirty-First International Joint Conference on
Artificial Intelligence, {IJCAI-22}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Lud De Raedt},
pages = {5521--5529},
year = {2022},
month = {7},
note = {Survey Track},
doi = {10.24963/ijcai.2022/772},
url = {https://doi.org/10.24963/ijcai.2022/772},
}