App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 12

Structure-guided graph convolutional networks for influential nodes ranking

Xiang Jin1
Liu Xiaoyang1
Cao Qiong1
Zhao Na2
1. School of Computer Science & Engineering, Chongqing University of Technology, Chongqing 400054, China
2. School of Software, Yunnan University Yunnan 650504, China
DOI: 10.19734/j.issn.1001-3695.2026.05.0109

Abstract

The existing evaluation indicators based on network centrality are difficult to describe the influence of high-order structure of complex networks on propagation, and the existing graph neural network methods often ignore the structural priors related to the propagation mechanism, which leads to insufficient performance in the stability and generalization ability of key node ranking. This paper proposed a structure-guided node propagation ability ranking model for complex networks (NGI-GCN) . Firstly, the node effectiveness, k-shell index and neighborhood influence were combined to construct the global structure index NGI of propagation awareness. Secondly, NGI was introduced into the message passing pro0063ess of Graph convolutional network, and NGI-Guided Graph Convolution was designed to make nodes with strong propagation potential obtain higher weights in representation learning, and the relative order of propagation ability between nodes was directly optimized by ranking learning. Finally, the comparative analysis is carried out on 12 real network datasets and 7 baseline models. The experimental results show that the proposed NGI-GCN method is significantly better than a variety of classical centrality methods and mainstream graph neural network models in terms of Kendall's τ, monotonicity and coverage, and the effect on different data sets is improved by 0.2%-10.07%, which indicates the rationality and effectiveness of the proposed NGI-GCN method.

Foundation Support

国家自然科学基金"基于相对重要性的复杂网络信息挖掘"(62366057)
重庆市教委重点项目"面向跨域信息融合的复杂网络关键节点识别技术"(KJZD-K202401101)
重庆市研究生科研创新项目"基于图表示学习的复杂网络关键节点识别方法"(CYS240679)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2026.05.0109
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 12

Publish History

[2026-08-04] Accepted Paper

Cite This Article

向进, 刘小洋, 曹琼, 等. 融合结构引导与图卷积网络的复杂网络关键节点识别方法 [J]. 计算机应用研究, 2026, 43 (12). (2026-08-25). https://doi.org/10.19734/j.issn.1001-3695.2026.05.0109. (Xiang Jin, Liu Xiaoyang, Cao Qiong, et al. Structure-guided graph convolutional networks for influential nodes ranking [J]. Application Research of Computers, 2026, 43 (12). (2026-08-25). https://doi.org/10.19734/j.issn.1001-3695.2026.05.0109. )

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  • Application Research of Computers Monthly Journal
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    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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