Survey on Graph Neural Network Acceleration: An Algorithmic Perspective


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.

31st International Joint Conference on Artificial Intelligence - Survey Track (IJCAI-22), July 23-29, 2022 Messe Wien, Vienna, Austria
Shirui Pan
Shirui Pan
Professor | ARC Future Fellow

My research interests include data mining, machine learning, and graph analysis.