基于结构感知的多图学习方法

付东来; 高泽安

doi:10.12263/DZXB.20230565

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基于结构感知的多图学习方法

学术论文 | 更新时间：2025-12-24

- 基于结构感知的多图学习方法
- Multi-Graph Learning Based on Structure-Aware
- 电子学报 2024年52卷第7期页码：2407-2417
- 作者机构：
  
  中北大学软件学院，山西太原 030051
- 作者简介：
  
  [ "付东来男，1979年出生.博士，副教授，硕士生导师.主要研究方向为众包计算、可信计算、机器学习、神经网络等.E-mail: fudonglai@nuc.edu.cn" ]
  [ "高泽安男，1997年8月出生于山西省吕梁市.现为中北大学软件工程专业硕士研究生.主要研究方向为机器学习、神经网络.E-mail: gaozean2022@163.com" ]
- 基金信息：
- DOI：10.12263/DZXB.20230565
  中图分类号： TP319;TP391.4
- 收稿：2023-06-16，
  
  修回：2024-03-28，
  
  纸质出版：2024-07-25
- 稿件说明：
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付东来, 高泽安. 基于结构感知的多图学习方法[J]. 电子学报, 2024, 52(07): 2407-2417.

FU Dong-lai, GAO Ze-an. Multi-Graph Learning Based on Structure-Aware[J]. Acta Electronica Sinica, 2024, 52(07): 2407-2417.
付东来, 高泽安. 基于结构感知的多图学习方法[J]. 电子学报, 2024, 52(07): 2407-2417. DOI：10.12263/DZXB.20230565

FU Dong-lai, GAO Ze-an. Multi-Graph Learning Based on Structure-Aware[J]. Acta Electronica Sinica, 2024, 52(07): 2407-2417. DOI：10.12263/DZXB.20230565

摘要

多图学习是一种非常重要的学习范式.与多示例学习相比，在多图学习中包表示一个对象，包中的每一个图对应一个子对象.这种数据表示方法能够表达子对象的结构信息.但是，现有的多图学习方法不仅隐含假设包内的图满足独立同分布，而且多采用将多图学习问题转变为多示例学习问题的技术思路.这类多图学习方法容易损失图自身及图间的结构信息.针对上述问题，本文提出一种基于结构感知的多图学习方法，有效学习图自身和图间的结构信息.该方法利用图核，通过计算图之间的相似度保留图自身的结构信息，通过生成包级图表达图间的结构信息，并且设计包编码器有效学习图间的结构信息.在NCI（1）、NCI（109）和AIDB三个多图数据集上的实验结果表明，所提方法相较于现有方法在准确率、精确率、

值和AUC上分别平均提高了5.97%、3.44%、4.48%和2.56%，在召回率上平均降低了2.12%.

Abstract

Multi-graph learning is a very important learning paradigm. Compared with multi-instance learning

in multi-graph learning

a bag represents an object

and each graph in the bag corresponds to a sub-object. This data representation method can express the structural inform

ation of sub-objects. However

existing multi-graph learning methods not only implicitly assume that the graphs in the bag satisfy independent and identical distribution

but also mostly adopt the technical idea of transforming multi-graph learning problems into multi-instance learning problems. This type of multi-graph learning method easily loses the structural information of the graph itself and the relationships between graphs. In response to the above problems

a multi-graph learning method based on structure awareness is proposed to effectively learn the structural information of the graph itself and the relationships between graphs. This method uses graph kernels to retain the structural information of the graph itself by calculating the similarity between graphs

expresses the structural information between graphs by generating bag-level graphs

and designs a bag encoder to effectively learn the structural information between graphs. Experimental results on the NCI(1)

NCI(109)

and AIDB datasets show that compared with existing methods

the proposed method improved by 5.97%

3.44%

4.48%

and 2.56% in accuracy

precision

value

and AUC respectively. In terms of recall rate decreased by 2.12%.

关键词

Keywords

references

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