Revisiting Neighborhood Aggregation in Graph Neural Networks for Node Classification using Statistical Signal Processing
<|reference_start|>Revisiting Neighborhood Aggregation in Graph Neural Networks for Node Classification using Statistical Signal Processing: 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.<|reference_end|>
@article{ghogho2024revisiting,
    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},
    archivePrefix={arXiv},
    eprint={2407.15284},
    primaryClass={cs.LG eess.SP stat.ML}
}