基于增强图神经网络和对比学习的复杂网络节点分类

徐培玲; 王玉; 谭艳丽

doi:10.11959/j.issn.1000-0801.2025128

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基于增强图神经网络和对比学习的复杂网络节点分类

研究与开发 | 更新时间：2025-09-04

- 基于增强图神经网络和对比学习的复杂网络节点分类
- Node classification of complex network based on enhanced graph neural network and contrastive learning
- 电信科学 2025年41卷第8期页码：127-138
- 作者机构：
  
  1.山西经贸职业学院电子信息系，山西太原 030024
  2.中北大学信息与通信工程学院，山西太原 030051
  3.太原工业学院电子工程系，山西太原 030000
- 作者简介：
  
  [ "徐培玲（1981- ），女，山西经贸职业学院电子信息系讲师，主要研究方向为电子信息与通信技术、信息处理及嵌入式系统。" ]
  [ "王玉（1978- ），女，博士，中北大学信息与通信工程学院副教授，主要研究方向为医学图像处理、激光信号处理。" ]
  [ "谭艳丽（1978- ），女，太原工业学院电子工程系教授，主要研究方向为深度学习、异常检测。" ]
- 基金信息：
  
  教育部产学合作协同育人项目(220606517245903)
- DOI：10.11959/j.issn.1000-0801.2025128
  中图分类号： TP393
- 收稿日期：2024-12-22，
  
  修回日期：2025-03-21，
  
  纸质出版日期：2025-08-20
- 稿件说明：
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徐培玲,王玉,谭艳丽.基于增强图神经网络和对比学习的复杂网络节点分类[J].电信科学,2025,41(08):127-138.

XU Peiling,WANG Yu,TAN Yanli.Node classification of complex network based on enhanced graph neural network and contrastive learning[J].Telecommunications Science,2025,41(08):127-138.
徐培玲,王玉,谭艳丽.基于增强图神经网络和对比学习的复杂网络节点分类[J].电信科学,2025,41(08):127-138. DOI： 10.11959/j.issn.1000-0801.2025128.

XU Peiling,WANG Yu,TAN Yanli.Node classification of complex network based on enhanced graph neural network and contrastive learning[J].Telecommunications Science,2025,41(08):127-138. DOI： 10.11959/j.issn.1000-0801.2025128.

摘要

复杂网络节点分类大多基于图神经网络学习节点表示而实现，图神经网络通过邻域聚合对复杂网络局部结构信息进行编码。然而，图神经网络的过平滑问题导致复杂网络节点分类性能受限。基于此，提出一种基于增强图神经网络和对比学习的复杂网络节点分类方法。该方法不仅为邻域节点引入注意力来区分各邻居节点的重要性，而且采用局部邻域重叠度和全局邻域重叠度构造边的特征，从而扩大节点表示的信息量。最后，引入对比学习对神经网络进行训练，从而利用网络全局节点分类先验信息对节点表示进行联合优化。在Cora、Citeseer、PubMed和Chameleon公开网络数据集上进行了实验，结果表明，相较于其他先进方法，所提方法的节点分类性能更好，并通过消融实验验证了所提方法的有效性。

Abstract

Node classification methods of complex network are mostly realized based on node representation learned by the graph neural network

the graph neural network encodes local structure information of complex networks through neighborhood aggregation. However

the over-smoothing problem of the graph neural network limits the node classification performance of complex network. In view of this problem

a node classification method of complex networks based on enhanced graph neural networks and contrastive learning was proposed. In the proposed method

not only the attention was introduced to the neighborhood nodes

in order to differentiate the importance of each neighbor node

but also the feature of each edge was constructed with combination of the local neighborhood overlap and the global neighborhood overlap

so as to expand the information of the node representation. Finally

contrastive learning was introduced to train the neural networks

so that the network’s global node priori information was utilized to jointly optimize the node representation. Experiments were performed on Cora

Citeseer

PubMed and Chameleon public network datasets. The results demonstrate that compared to the other advanced methods

the proposed method achieves better node classification performance

moreover

the effectiveness of the proposed method is verified through ablation study.

关键词

Keywords

references

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