Stop Blaming Aggregation in Deep GNNs Based on Incomplete Evidence

This paper argues that aggregation mechanisms in Graph Neural Networks (GNNs) have been unfairly blamed for limiting network depth based on incomplete evidence. The prevailing narrative has led researchers to view aggregation as fundamentally problematic for deep GNNs. However, through reexamining the experimental evidence and theoretical explanations by proper ablations, we show that aggregation's negative effects have been exaggerated while its benefits have been overlooked. Based on our findings, we advocate two actions: First, practitioners should actively deploy deep GNNs on tasks that require large capacity or long-range dependencies, whose potential has already been demonstrated yet remains overlooked. Second, researchers should rigorously investigate the true effects of aggregation on deep GNNs rather than accepting conventional wisdom.