Examining the Efficacy of Graph Neural Network Message-Passing in Regression Contexts

2026-07-28

AI Summary

This study addresses the lack of systematic evaluation of graph neural networks (GNNs) in regression tasks, where existing benchmarks have predominantly focused on classification, often leading to the inappropriate adoption of classification-oriented architectures. For the first time, this work comprehensively assesses diverse GNN message-passing mechanisms on regression performance across multiple dimensions—including ranking capability, error minimization, and interpretability—encompassing deep convolutional models (e.g., GEN), attention-based approaches, and classical theory-driven architectures. The empirical results demonstrate that deep convolutional GNNs significantly outperform attention-based models in regression settings, while certain classical GNNs remain competitive without sacrificing efficiency. These findings provide practitioners with evidence-based guidance for model selection and architectural design tailored specifically to regression tasks on graph-structured data.

Abstract

Graph Neural Networks (GNN) facilitate effective prediction on graph data such as molecules, media networks and neural network blueprints. GNNs facilitate prediction through message passing techniques which define how information flows from a node to its neighbors. Due to the ubiquity of the graph data type, the development of newer and better GNNs has garnered much interest in the machine learning community. However, GNN evaluation and benchmarking is primarily driven by classification tasks. Thus, prospective GNN message passing layers are evaluated on their ability to outperform prior work in classification contexts. In contrast, GNNs are equally capable of performing scalar regression prediction, yet this class of problem is often overlooked when proposing new GNNs while the best classification GNNs are utilized in an a priori or off-the-shelf manner for regression problems. In response, this paper studies the efficacy of GNN layers in a slew of regression contexts from rank ordering, error minimization and insight extraction. Results show that deep convolutional GNNs, particularly GEN, are more effective at these tasks than attention-based GNNs, while other classical, theoretically-inspired GNNs remain competitive and efficient.

Problem

• Graph Neural Networks
• Regression
• Message Passing
• Benchmarking
• Classification Bias

Innovation

• Graph Neural Networks
• Message Passing
• Regression Tasks
• GEN
• Attention-based GNNs