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.

翻译：本文深入探讨图结构中的节点分类问题，特别针对图神经网络（GNNs）的核心组成部分——邻域聚合机制进行重新审视。我们的分析表明，在边独立节点标签假设下（该假设常见于节点分类基准图数据中），部分基准GNN模型存在概念性缺陷。通过从统计信号处理的角度研究邻域聚合机制，本工作提出了可用于设计更高效GNN模型的新见解。