论文标题
图形神经网络加速度的调查：算法观点
Survey on Graph Neural Network Acceleration: An Algorithmic Perspective
论文作者
论文摘要
图神经网络（GNNS）一直是最近研究的热点，并在不同的应用中广泛使用。但是，随着使用Huger数据和更深层次的模型，毫不奇怪地提出了紧急需求以加速GNN，以提高执行力。在本文中，我们从算法的角度提供了有关GNN的加速方法的全面调查。我们首先提出了一种新的分类法，将现有加速方法分为五类。基于分类，我们系统地讨论这些方法并突出它们的相关性。接下来，我们提供了这些方法的效率和特征方面的比较。最后，我们建议对未来研究的一些有希望的前景。
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.
