对比不同图神经网络在回归任务中的表现，发现深度卷积模型更优。
Examining the Efficacy of Graph Neural Network Message-Passing in Regression Contexts
- 比较深度卷积与注意力机制GNN在回归任务中的信息传递效果
- GEN模型在排序、误差最小化等任务上优于注意力型GNN
- 为回归问题提供可直接使用的高效图神经网络方案
图神经网络（GNN）通过消息传递机制在分子、媒体网络和神经网络结构等图数据上实现有效预测。尽管其广泛应用于分类任务，但多数新GNN模型仍以分类性能为评估标准，而回归任务常被忽视。本文系统评估了多种GNN在回归场景下的表现，涵盖排序、误差最小化与洞察提取等目标。结果表明，深度卷积型GNN（如GEN）在这些任务中显著优于注意力机制类模型，而部分经典理论驱动的GNN也保持良好竞争力与计算效率。
原文摘要 · Abstract (English)
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.
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