Survey on Graph Neural Network Acceleration: An Algorithmic Perspective
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.
Cite
Text
Liu et al. "Survey on Graph Neural Network Acceleration: An Algorithmic Perspective." International Joint Conference on Artificial Intelligence, 2022. doi:10.24963/IJCAI.2022/772Markdown
[Liu et al. "Survey on Graph Neural Network Acceleration: An Algorithmic Perspective." International Joint Conference on Artificial Intelligence, 2022.](https://mlanthology.org/ijcai/2022/liu2022ijcai-survey/) doi:10.24963/IJCAI.2022/772BibTeX
@inproceedings{liu2022ijcai-survey,
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 = {International Joint Conference on Artificial Intelligence},
year = {2022},
pages = {5521-5529},
doi = {10.24963/IJCAI.2022/772},
url = {https://mlanthology.org/ijcai/2022/liu2022ijcai-survey/}
}