一种双通道半监督网络表示学习模型

杜航原; 谢富中; 王文剑; 白亮

doi:10.11959/j.issn.2096-0271.2024052

您当前的位置：

首页 >

文章列表页 >

一种双通道半监督网络表示学习模型

研究 | 更新时间：2024-08-07

- 一种双通道半监督网络表示学习模型
- A dual channel semi-supervised network representation learning model
- 大数据 2024年10卷第4期页码：106-120
- 作者机构：
  
  1. 山西大学计算机与信息技术学院，山西太原 030006
  2. 山西大学智能信息处理研究所，山西太原 030006
- 作者简介：
  
  [ "杜航原（1985- ），男，博士，山西大学计算机与信息技术学院副教授，主要研究方向为图机器学习理论及应用。" ]
  [ "谢富中（1998- ），男，山西大学计算机与信息技术学院硕士生，主要研究方向为机器学习、网络数据挖掘。" ]
  [ "王文剑（1968- ），女，博士，山西大学计算机与信息技术学院教授、院长，主要研究方向为机器学习、数据挖掘、计算智能。" ]
  [ "白亮（1982- ），男，博士，山西大学智能信息处理研究所教授、所长，主要研究方向为机器学习与数据挖掘。" ]
- 基金信息：
  
  国家自然科学基金项目;The National Natural Science Foundation of China(U21A20513);国家自然科学基金项目;The National Natural Science Foundation of China(62076154);国家自然科学基金项目;The National Natural Science Foundation of China(62276159);国家自然科学基金项目;The National Natural Science Foundation of China(62276161);山西省重点研发计划项目;The Key R＆D Program of Shanxi Province(202302010101007);山西省重点研发计划项目;The Key R＆D Program of Shanxi Province(202202020101003);山西省基础研究计划项目;The Fundamental Research Program of Shanxi Province(202303021221055)
- DOI：10.11959/j.issn.2096-0271.2024052
  中图分类号： TP391
- 网络首发：2024-07，
  
  纸质出版：2024-07-15
- 稿件说明：
移动端阅览
杜航原, 谢富中, 王文剑, 等. 一种双通道半监督网络表示学习模型[J]. 大数据, 2024,10(4):106-120.

Hangyuan DU, Fuzhong XIE, Wenjian WANG, et al. A dual channel semi-supervised network representation learning model[J]. Big data research, 2024, 10(4): 106-120.
杜航原, 谢富中, 王文剑, 等. 一种双通道半监督网络表示学习模型[J]. 大数据, 2024,10(4):106-120. DOI： 10.11959/j.issn.2096-0271.2024052.

Hangyuan DU, Fuzhong XIE, Wenjian WANG, et al. A dual channel semi-supervised network representation learning model[J]. Big data research, 2024, 10(4): 106-120. DOI： 10.11959/j.issn.2096-0271.2024052.

摘要

在半监督网络表示学习中，节点标签对于网络在不同空间中映射关系的建立具有重要指导意义。然而在很多实际任务中，可用标签信息往往比较有限或难以获取，这导致在学习网络低维表示的过程中无法提供充分有效的监督。针对这一问题，提出了一种双通道半监督网络表示学习模型，该模型以自编码器为基本框架，由自监督和半监督两个信息传递通道构成。自监督信号与标签信息分别在两个通道中对网络表示映射关系的建立提供指导，同时二者之间形成信息互补与增强。考虑到两个通道间可能存在信息冗余，在互信息视角下设计了冗余识别与消除机制。在此基础上，构造了一体化优化模型，实现自监督学习与半监督学习的协同，使学习到的网络表示更好地捕捉和保持网络的结构和特性。在真实数据集上的实验结果表明，提出的模型学习的网络表示在节点分类、聚类和可视化等任务中能够获得优于基线方法的性能。

Abstract

In semi-supervised network representation learning

node labels play an important role in guiding the establishment of network mapping relationships among different spaces.However

in many practical tasks

the available label information is usually limited or difficult to obtain

which makes it difficult to provide sufficient and effective supervision to the process of learning low-dimensional network representations.In order to solve this problem

a dual channel semisupervised network representation learning model is proposed

which is composed of two information transmission channels

namely self-supervised and semi-supervised channels

with the framework of autoencoder.The self-supervised information and the label information provide guidance for the establishment of network representation mapping in the two channels respectively

and they form such a sense of information complementation and enhancement for the learning process.Considering the possible information redundancy between the two channels

a redundancy recognition and elimination mechanism is designed in the perspective of mutual information.On this basis

an integrated optimization model is constructed to combine the self-supervised learning and the semi-supervised learning into a collaborative mechanism

which enables the learned representations to capture and preserve the structure and characteristics of the network.Experimental results on several real datasets show that the network representations learned by the proposed model can achieve better performance than baseline methods in node classification

clustering and visualization tasks.

关键词

Keywords

references

涂存超 , 杨成 , 刘知远 , 等 . 网络表示学习综述 [J ] . 中国科学:信息科学 , 2017 , 47 ( 8 ): 980 - 996 .

TU C C , YANG C , LIU Z Y , et al . Network representation learning:an overview [J ] . Scientia Sinica (Informationis) , 2017 , 47 ( 8 ): 980 - 996 .

戴筠 . 基于双曲空间图嵌入的科研热点预测 [J ] . 大数据 , 2022 , 8 ( 6 ): 94 - 104 .

DAI J . Emerging scientific topic prediction based on Poincare graph embedding [J ] . Big Data Research , 2022 , 8 ( 6 ): 94 - 104 .

CAO S S , LU W , XU Q K . GraRep:learning graph representations with global structural information [C ] // Proceedings of the 24th ACM International on Conference on Information and Knowledge Management . New York:ACM , 2015 : 891 - 900 .

PEROZZI B , AL-RFOU R , SKIENA S . DeepWalk:online learning of social representations [C ] // Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining . New York:ACM , 2014 : 701 - 710 .

WANG D X , CUI P , ZHU W W . Structural deep network embedding [C ] // Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . New York:ACM , 2016 : 1225 - 1234 .

TU C C , ZHANG W C , LIU Z Y , et al . Max-margin deepwalk:discriminative learning of network representation [C ] // Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence . New York:ACM , 2016 : 3889 - 3895 .

KIPF T N , WELLING M . Semi-supervised classification with graph convolutional networks [EB ] . arXiv preprint,2016,arXiv:1609.02907 .

YANG Z L , COHEN W W , SALAKHUTDINOV R . Revisiting semi-supervised learning with graph embeddings [C ] // Proceedings of the 33rd International Conference on International Conference on Machine Learning . New York:ACM , 2016 : 40 - 48 .

FAN S H , WANG X , SHI C , et al . Debiased graph neural networks with agnostic label selection bias [J ] . IEEE Transactions on Neural Networks and Learning Systems , 2022 , 1 - 12 .

ZHANG H G , WANG Z S , LIU D R . A comprehensive review of stability analysis of continuous-time recurrent neural networks [J ] . IEEE Transactions on Neural Networks and Learning Systems , 2014 , 25 ( 7 ): 1229 - 1262 .

CHEN L Y , LI S B , BAI Q , et al . Review of image classification algorithms based on convolutional neural networks [J ] . Remote Sensing , 2021 , 13 ( 22 ): 4712 .

ZHANG G J , LIU Y , JIN X N . A survey of autoencoder-based recommender systems [J ] . Frontiers of Computer Science , 2020 , 14 ( 2 ): 430 - 450 .

RUIZ L , GAMA F , RIBEIRO A . Gated graph recurrent neural networks [J ] . IEEE Transactions on Signal Processing , 2020 , 68 : 6303 - 6318 .

GAO H Y , WANG Z Y , JI S W . Largescale learnable graph convolutional networks [C ] // Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery ＆ Data Mining . New York:ACM , 2018 : 1416 - 1424 .

WENG Z Q , ZHANG W Y , DOU W . Adversarial attention-based variational graph autoencoder [J ] . IEEE Access , 2020 , 8 : 152637 - 152645 .

CHENG D W , WANG X Y , ZHANG Y , et al . Graph neural network for fraud detection via spatial-temporal attention [J ] . IEEE Transactions on Knowledge and Data Engineering , 2022 , 34 ( 8 ): 3800 - 3813 .

ZHOU J , CUI G Q , HU S D , et al . Graph neural networks:a review of methods and applications [J ] . AI Open , 2020 , 1 : 57 - 81 .

WU Z H , PAN S R , CHEN F W , et al . A comprehensive survey on graph neural networks [J ] . IEEE Transactions on Neural Networks and Learning Systems , 2021 , 32 ( 1 ): 4 - 24 .

WU L R , LIN H T , TAN C , et al . Selfsupervised learning on graphs:contrastive,generative,or predictive [J ] . IEEE Transactions on Knowledge and Data Engineering , 2023 , 35 ( 4 ): 4216 - 4235 .

PENG Z , HUANG W B , LUO M N , et al . Graph representation learning via graphical mutual information maximization [C ] // Proceedings of The Web Conference 2020 . New York:ACM , 2020 : 259 - 270 .

VELIČKOVIĆ P , FEDUS W , HAMILTON W L , et al . Deep graph infomax [C ] // Proceedings of the 7th International Conference on Learning Representations . arXiv preprint , 2019 ,arXiv:1809.10341.

SUN F , HOFFMANN J , VERMA V , et al . InfoGraph:unsupervised and semisupervised graph-level representation learning via mutual information maximization [EB ] . arXiv preprint,2019,arXiv:1908.01000 .

BELGHAZI M I , BARATIN A , RAJESWAR S , et al . MINE:mutual information neural estimation [EB ] . arXiv preprint,2018,arXiv:1801.04062 .

HJELM R D , FEDOROV A , LAVOIEMARCHILDON S , et al . Learning deep representations by mutual information estimation and maximization [EB ] . arXiv preprint,2018,arXiv:1808.06670 .

QIN Z , KIM D , GEDEON T , et al . Rethinking softmax with cross-entropy:neural network classifier as mutual information estimator [EB ] . arXiv preprint,2018,arXiv:1911.10688 .

HU Y , YOU H X , WANG Z C , et al . Graph-MLP:node classification without message passing in graph [EB ] . arXiv preprint,2021,arXiv:2106.04051 .

XU B B , SHEN H W , CAO Q , et al . Graph wavelet neural network [EB ] . arXiv preprint,2019,arXiv:1904.07785 .

KANG Z , LIN Z P , ZHU X F , et al . Structured graph learning for scalable subspace clustering:from single view to multiview [J ] . IEEE Transactions on Cybernetics , 2022 , 52 ( 9 ): 8976 - 8986 .

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于One-Class学习的鲁棒音频真伪识别

知识图谱推理：现代的方法与应用