Revisiting Neighborhood Aggregation in Graph Neural Networks for Node Classification using Statistical Signal Processing

• URL: http://arxiv.org/abs/2407.15284v1
• Date: Sun, 21 Jul 2024 22:37:24 GMT
• Title: Revisiting Neighborhood Aggregation in Graph Neural Networks for Node Classification using Statistical Signal Processing
• Authors: Mounir Ghogho,
• Abstract summary: We 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.
• Score: 4.184419714263417
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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