2 papers
cs.LG2026
How Much Does Message Passing Matter? A Drop-In Study of GNN Layers for Neural Network Graph Regression
Keith G. Mills, Aedan J. DeFrates, Joong Ho Kim
Graph Neural Networks (GNNs) are widely used as regressors, for example to predict the accuracy of a neural architecture. Yet new message-passing (MP) layers are developed and benc…
cs.LG2025
Model-Level GNN Explanations via Rule-to-Graph Readout for Logit Reconstruction
Shengyao Lu, Jiuding Yang, Aedan J. DeFrates +3
We propose a novel model-level GNN explanation framework that shifts the explanation target from class-wise rule extraction to rule-based logit reconstruction. Our method recasts t…