Characterizing graph neural networks (GNNs) is essential for identifying performance bottlenecks and facilitating their deployment. Despite substantial work in this area, a comprehensive survey on GNN characterization is lacking. This work presents a comprehensive survey, proposing a triple-level classification method to categorize, summarize, and compare existing efforts. In addition, we identify promising future directions for GNN characterization. Our survey aims to help scholars systematically understand GNN performance bottlenecks and patterns from a computer architecture perspective, contributing to more efficient GNN execution.

翻译：图神经网络（GNN）的特征分析对于识别性能瓶颈、促进其部署至关重要。尽管该领域已有大量研究工作，但目前仍缺乏关于GNN特征分析的系统性综述。本文提出一项综合性综述，采用三级分类方法对现有工作进行分类、总结与比较。此外，我们指出了GNN特征分析未来具有前景的研究方向。本综述旨在帮助学者从计算机体系结构视角系统理解GNN的性能瓶颈与模式，从而为提升GNN执行效率作出贡献。