How Much Does Message Passing Matter? A Drop-In Study of GNN Layers for Neural Network Graph Regression
Read the original on arXiv Machine Learning →The study investigates how different message‑passing (MP) layers affect graph‑level regression performance in Graph Neural Networks (GNNs). By replacing the MP component in four existing GNN regressors with ten varied configurations—including convolutional, isomorphism‑based, and attention‑based designs—the authors evaluate performance across eleven neural‑network graph datasets, measuring metrics such as rank correlation, prediction error, top‑k retrieval, latency, and memory usage. Results show that the choice of MP layer significantly influences outcomes, with the optimal layer depending on graph size, training‑set size, and regression objective; classical layers like GEN, k‑GNN, and PNA outperform attention‑based layers on small graphs, while GATv2 excels on datasets with few large graphs.
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