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

AI 中文解读

一项最新研究揭示，图神经网络（GNN）在预测连续数值（比如温度、价格）的任务中，可能比当前最热门的注意力机制模型更靠谱。GNN擅长处理像社交网络、分子结构这类由“点和线”组成的关系数据，它通过“消息传递”让相邻节点交换信息，从而做出预测。但长期以来，业界评测GNN好坏的标准几乎都是图像分类这类“对错题”，而忽略了回归预测这类“计算题”（例如预测某个分子的性质）。这篇论文系统对比了多种GNN在回归任务中的表现，发现深度卷积类GNN（尤其是GEN模型）在数值排序、误差控制等方面明显优于注意力类GNN，而一些经典理论驱动的GNN也保持了不错的效率和竞争力。这对普通人意味着什么呢？未来像药物研发、材料筛选、天气预测乃至推荐系统这类需要输出精确数值的场景，可能因选用更合适的GNN而变得更准确、更可靠。不再盲目追新，而是根据任务性质“量体裁衣”，AI将更聪明地为生活服务。

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.