AI 中文总结
提出节点级图神经架构搜索（N-GNAS）算法，为节点子集自动选择架构并引入对比学习损失，在八个数据集上超越现有方法，CiteSeer准确率达78.26%。
AI 中文摘要
近年来，图神经网络（GNNs）和架构搜索框架因其在处理非结构化数据方面的卓越能力，在非欧几里得数据处理中得到了广泛应用。然而，传统方法通常对所有节点应用统一的卷积操作，而不考虑其不同的结构和特征特性，这可能削弱模型性能，并随着层数增加导致过平滑问题。为了克服这一局限，在本工作中，我们提出了一种节点级图神经架构搜索（N-GNAS）算法。它可以在更新节点特征时，为每个节点子集自动选择合适的网络架构。N-GNAS还引入了对比学习损失，以分离不同类别的样本特征，反之亦然。在八个数据集上进行的节点和图分类实验中，我们的方法优于当前领先的GNAS技术和传统人工设计的GNNs。例如，在CiteSeer数据集上，它达到了78.26%的准确率。
英文摘要
In recent years, Graph Neural Networks (GNNs) and architecture search frameworks have gained extensive application in non-Euclidean data processing, attributable to their superior capacity in managing unstructured data. Nevertheless, traditional approaches typically apply uniform convolution operations to all nodes, regardless of their varying structural and feature characteristics, which can undermine model performance and result in over-smoothing issues as the number of layers increases. To overcome this limitation, in this work, we propose a \textbf{N}ode-Level \textbf{G}raph \textbf{N}eural \textbf{A}rchitecture \textbf{S}earch (N-GNAS) algorithm. It can automatically choose an appropriate network architecture for each subset of nodes when updating node features. N-GNAS also introduces a contrastive learning loss to separate sample features from different categories and vice versa. In experiments conducted on eight datasets for node and graph classification, our methodology outperforms current leading GNAS techniques and traditional human-designed GNNs. For example, it achieves an accuracy rate of 78.26\% on the CiteSeer dataset.
