  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.
