We delve into the issue of node classification within graphs, specifically reevaluating the concept of neighborhood aggregation, which is a fundamental component in graph neural networks (GNNs). Our analysis reveals conceptual flaws within certain benchmark GNN models when operating under the assumption of edge-independent node labels, a condition commonly observed in benchmark graphs employed for node classification. Approaching neighborhood aggregation from a statistical signal processing perspective, our investigation provides novel insights which may be used to design more efficient GNN models.
@article{arxiv.2407.15284,
title = {Revisiting Neighborhood Aggregation in Graph Neural Networks for Node Classification using Statistical Signal Processing},
author = {Mounir Ghogho},
journal= {arXiv preprint arXiv:2407.15284},
year = {2024}
}