Revisiting Neighborhood Aggregation in Graph Neural Networks for Node Classification using Statistical Signal Processing
This addresses a foundational issue in graph neural networks for researchers, but it appears incremental as it revisits existing concepts without reporting new experimental results.
The paper tackled node classification in graphs by reevaluating neighborhood aggregation in GNNs, revealing conceptual flaws in benchmark models under edge-independent label assumptions and providing insights for more efficient designs.
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.