Revisiting Graph Neural Networks: All We Have is Low-Pass Filters

Understand

Graph neural networks have become one of the most important techniques to solve machine learning problems on graph-structured data.

• Recent work on vertex classification proposed deep and distributed learning models to achieve high performance and scalability.
• However, we find that the feature vectors of benchmark datasets are already quite informative for the classification task, and the graph structure only provides a means to denoise the data.
• In this paper, we develop a theoretical framework based on graph signal processing for analyzing graph neural networks.