Abstract
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.
Keywords
Publication details
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Cite this article
APA 7
Yanga, L., Lia, S., Wang, Y., Liò, P., Shen, X., Liu, B., & Peng, C. (2026). Node-level Graph Neural Architecture Search Framework. https://omanscience.com/en/articles/node-level-graph-neural-architecture-search-framework
MLA 9
Yanga, Lintao, et al. "Node-level Graph Neural Architecture Search Framework." https://omanscience.com/en/articles/node-level-graph-neural-architecture-search-framework.
Chicago (author–date)
Yanga, Lintao, Sirui Lia, Yaqing Wang, Pietro Liò, Xu Shen, Baisong Liu, and Chengbin Peng. 2026. "Node-level Graph Neural Architecture Search Framework." https://omanscience.com/en/articles/node-level-graph-neural-architecture-search-framework.
Harvard
Yanga, L., Lia, S., Wang, Y., Liò, P., Shen, X., Liu, B. and Peng, C. (2026) 'Node-level Graph Neural Architecture Search Framework', Available at: https://omanscience.com/en/articles/node-level-graph-neural-architecture-search-framework.
Vancouver
Yanga L, Lia S, Wang Y, Liò P, Shen X, Liu B, et al. Node-level Graph Neural Architecture Search Framework. https://omanscience.com/en/articles/node-level-graph-neural-architecture-search-framework
IEEE
L. Yanga, S. Lia, Y. Wang, P. Liò, X. Shen, B. Liu, and C. Peng, "Node-level Graph Neural Architecture Search Framework," https://omanscience.com/en/articles/node-level-graph-neural-architecture-search-framework.