  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.
