aiqbench analysis
This research addresses the overlooked gap in Graph Neural Network (GNN) benchmarking by shifting focus from classification dominance to scalar regression tasks. The study reveals that deep convolutional architectures, specifically GEN, outperform attention-based mechanisms for error minimization and rank ordering on graph-structured data like molecules and media networks. For practitioners building predictive models on non-tabular data, these findings suggest revisiting classical or convolutional message-passing layers rather than defaulting to standard classification-focused GNNs.
Examining the Efficacy of Graph Neural Network Message-Passing in Regression Contexts
arXiv:2607.26404v1 Announce Type: new 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.
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.
Machine Learning (cs.LG)
arXiv:2607.26404 [cs.LG]
(or arXiv:2607.26404v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.26404 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 275 words
Related coverage
Edge-Girth as a Structural Edge Feature for Graph Neural Networks
arXiv · Sep 2, 2026
Reference-Tail Trust:Certified Probability Floors for Learned Updates Inside a Deployed Network
arXiv · Sep 30, 2026
Quantile-Free Uncertainty Quantification in Graph Neural Networks
arXiv · Jun 15, 2026
When Vision Meets Graphs: A Survey on Graph Reasoning and Learning
arXiv · Sep 4, 2026