Mitigating Redundancy in Graph Neural Networks for Improved Expressivity and Accuracy

Redundancy in the information flow and computation of graph neural networks can lead to oversquashing, limiting their expressivity and accuracy. The proposed DAG-MLP approach systematically eliminates redundant information by using neighborhood trees and exploits computational redundancy through merging of isomorphic subtrees, achieving higher expressivity and accuracy compared to standard graph neural networks.