Survey on Graph Neural Network Acceleration: An Algorithmic Perspective
It addresses the urgent demand for accelerating GNNs due to larger data and deeper models, but it is incremental as it surveys existing work rather than proposing new methods.
This paper provides a comprehensive survey on acceleration methods for Graph Neural Networks (GNNs) from an algorithmic perspective, presenting a new taxonomy to classify existing methods and discussing their correlations and comparisons.
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.