超越同质性假设的双通道属性图聚类

安俊秀; 柳源; 杨林旺

doi:10.11959/j.issn.1000-0801.2025009

您当前的位置：

首页 >

文章列表页 >

超越同质性假设的双通道属性图聚类

研究与开发 | 更新时间：2025-02-07

- 超越同质性假设的双通道属性图聚类
- Dual-channel attribute graph clustering beyond the homogeneity assumption
- 电信科学 2025年41卷第1期页码：111-124
- 作者机构：
  
  成都信息工程大学软件工程学院，四川成都 610207
- 作者简介：
  
  [ "安俊秀（1970- ），女，成都信息工程大学软件工程学院教授，主要研究方向为云计算与大数据技术、人工智能。" ]
  [ "柳源（1999- ），男，成都信息工程大学软件工程学院硕士生，主要研究方向为深度聚类、对比学习。" ]
  [ "杨林旺（2000- ），男，成都信息工程大学软件工程学院硕士生，主要研究方向为深度聚类、数据挖掘、自然语言处理。" ]
- 基金信息：
  
  国家社会科学基金资助项目(22BXW048)
- DOI：10.11959/j.issn.1000-0801.2025009
  中图分类号： TP391
- 收稿日期：2024-07-31，
  
  修回日期：2024-11-06，
  
  纸质出版日期：2025-01-20
- 稿件说明：
移动端阅览
安俊秀,柳源,杨林旺.超越同质性假设的双通道属性图聚类[J].电信科学,2025,41(01):111-124.

AN Junxiu,LIU Yuan,YANG Linwang.Dual-channel attribute graph clustering beyond the homogeneity assumption[J].Telecommunications Science,2025,41(01):111-124.
安俊秀,柳源,杨林旺.超越同质性假设的双通道属性图聚类[J].电信科学,2025,41(01):111-124. DOI： 10.11959/j.issn.1000-0801.2025009.

AN Junxiu,LIU Yuan,YANG Linwang.Dual-channel attribute graph clustering beyond the homogeneity assumption[J].Telecommunications Science,2025,41(01):111-124. DOI： 10.11959/j.issn.1000-0801.2025009.

摘要

属性图聚类的研究近些年取得了显著进步，但现有方法大多基于同质性假设，忽略了异质图的应用场景，导致在聚类过程中高频信息的丢失和聚类效果不佳。为解决此问题，提出了一种新颖的双通道属性图聚类方法（DCAGC）。该方法采用混合高斯模型预测节点连接的同质性，并基于这一预测构建同质和异质两种视图，以便从不同角度捕捉图中的低频和高频信息。同时，通过融合对比学习和聚类，实现了更精准的节点嵌入。与其他方法相比，DCAGC在处理异质图数据集时聚类效果显著，且具有较强的抗异常连接能力。

Abstract

In recent years

significant progress has been made in the research of attribute graph clustering. However

existing methods are mostly based on the homogeneity assumption

thereby neglecting the application scenarios of heterogeneous graphs

leading to the loss of high-frequency information and poor clustering results during the clustering process. To address this issue

a novel dual-channel attribute graph clustering (DCAGC) method was proposed. A mixture of Gaussian models was used to predict the homogeneity of node connections and two views of homogeneous and heterogeneous were built

based on this prediction to capture low-frequency and high-frequency information in the graph from different perspectives. Simultaneously

by integrating contrastive learning and clustering

more precise node embeddings were achieved. Compared to other methods

DCAGC demonstrates significant clustering performance when handling heterogeneous graph datasets and exhibits strong resilience to anomalous connections.

关键词

Keywords

references

NGUYEN V H , SUGIYAMA K , NAKOV P , et al . FANG: leveraging social context for fake news detection using graph representation [C ] // Proceedings of the 29th ACM International Conference on Information & Knowledge Management . New York : ACM Press , 2020 : 1165 - 1174 .

FAN S H , WANG X , SHI C , et al . One2Multi graph autoencoder for multi-view graph clustering [C ] // Proceedings of the Web Conference 2020 . New York : ACM Press , 2020 : 3070 - 3076 .

FANG R Y , WEN L J , KANG Z , et al . Structure-preserving graph representation learning [C ] // Proceedings of the 2022 IEEE International Conference on Data Mining (ICDM) . Piscataway : IEEE Press , 2022 : 927 - 932 .

KULATILLEKE G K , PORTMANN M , CHANDRA S S . SCGC: self-supervised contrastive graph clustering [EB ] . 2022 : 2204 .12656.

PEI H B , WEI B Z , CHANG K C C , et al . Geom-GCN: geometric graph convolutional networks [EB ] . 2020 : 2002 .05287.

KIPF T N , WELLING M . Variational graph auto-encoders [EB ] . 2016 : 1611 .07308.

DEVVRIT F , SINHA A , DHILLON I , et al . S3GC: scalable self-supervised graph clustering [J ] . Advances in Neural Information Processing Systems , 2022 , 35 : 3248 - 3261 .

WANG Y , DERR T . Tree decomposed graph neural network [C ] // Proceedings of the 30th ACM International Conference on Information & Knowledge Management . New York : ACM Press , 2021 : 2040 - 2049 .

WANG X , ZHANG M . How powerful are spectral graph neural networks [C ] // Proceedings of the International Conference on Machine Learning . Piscataway : IEEE Press , 2022 : 23341 - 23362 .

WANG C , PAN S R , HU R Q , et al . Attributed graph clustering: a deep attentional embedding approach [EB ] . 2019 : 1906 .06532.

BO D , WANG X , SHI C , et al . Structural deep clustering network [C ] // Proceedings of the Web Conference 2020 . New York : ACM Press , 2020 : 1400 - 1410 .

KANG Z , LIU Z Y , PAN S R , et al . Fine-grained attributed graph clustering [C ] // Proceedings of the 2022 SIAM International Conference on Data Mining (SDM) . Philadelphia : Society for Industrial and Applied Mathematics , 2022 : 370 - 378 .

LIU Y , TU W X , ZHOU S H , et al . Deep graph clustering via dual correlation reduction [C ] // Proceedings of the AAAI Conference on Artificial Intelligence . Palo Alto : AAAI Press , 2022 , 36 ( 7 ): 7603 - 7611 .

曹付元 , 陈晓惠 . 共享和特定表示的多视图属性图聚类 [J ] . 软件学报 , 2024 : 1 - 14 .

CAO F Y , CHEN X H . Multi-view attributed graph clustering based on shared and specific representation [J ] . Journal of Software , 2024 : 1 - 14 .

CHEN T , KORNBLITH S , NOROUZI M , et al . A simple framework for contrastive learning of visual representations [C ] // Proceedings of the International Conference on Machine Learning . Piscataway : IEEE Press , 2020 : 1597 - 1607 .

岑科廷 , 沈华伟 , 曹婍 , 等 . 图对比学习综述 [J ] . 中文信息学报 , 2023 , 37 ( 5 ): 1 - 21 .

CEN K T , SHEN H W , CAO Q , et al . A survey on graph contrastive learning [J ] . Journal of Chinese Information Processing , 2023 , 37 ( 5 ): 1 - 21 .

尹梦冉 , 梁美玉 , 于洋 , 等 . 面向跨模态检索的查询感知双重对比学习网络 [J ] . 软件学报 , 2024 , 35 ( 5 ): 2120 - 2132 .

YIN M R , LIANG M Y , YU Y , et al . Query aware dual contrastive learning network for cross-modal retrieval [J ] . Journal of Software , 2024 , 35 ( 5 ): 2120 - 2132 .

张颖 , 张冰冰 , 董微 , 等 . 基于语言-视觉对比学习的多模态视频行为识别方法 [J ] . 自动化学报 , 2024 , 50 ( 2 ): 417 - 430 .

ZHANG Y , ZHANG B B , DONG W , et al . Multi-modal video action recognition method based on language-visual contrastive learning [J ] . Acta Automatica Sinica , 2024 , 50 ( 2 ): 417 - 430 .

柳源 , 安俊秀 , 杨林旺 . 多角度语义标签引导的自监督多视图聚类 [J ] . 计算机应用研究 , 2024 : 1 - 8 .

LIU Y , AN J X , YANG L W . Multi-view clustering with self-supervised learning guided by multi-angle semantic labels [J ] . Application Research of Computers , 2024 : 1 - 8 .

LIU Y , YANG X H , ZHOU S H , et al . Simple contrastive graph clustering [J ] . IEEE Transactions on Neural Networks and Learning Systems , 2024 , 35 ( 10 ): 13789 - 13800 .

SHEN X , SUN D W , PAN S R , et al . Neighbor contrastive learning on learnable graph augmentation [C ] // Proceedings of the AAAI Conference on Artificial Intelligence . Palo Alto : AAAI Press , 2023 , 37 ( 8 ): 9782 - 9791 .

XIA W , WANG Q Q , GAO Q X , et al . Self-consistent contrastive attributed graph clustering with pseudo-label prompt [J ] . IEEE Transactions on Multimedia , 2023 , 25 : 6665 - 6677 .

LI Y F , HU P , LIU Z T , et al . Contrastive clustering [C ] // Proceedings of the AAAI Conference on Artificial Intelligence . Palo Alto : AAAI Press 2021 , 35 ( 10 ): 8547 - 8555 .

钱李烽 , 李静 , 邹徐熹 , 等 . 基于双重对比学习和硬样本挖掘的多视图图聚类算法 [J ] . 计算机工程 , 2024 : 1 - 16 .

QIANG L F , LI J , ZOU X X , et al . Multi-view graph clustering algorithm based on dual contrastive learning and hard sample mining [J ] . Computer Engineering , 2024 : 1 - 16 .

DEMPSTER A P , LAIRD N M , RUBIN D B . Maximum likelihood from incomplete data via the EM algorithm [J ] . Journal of the Royal Statistical Society Series B: Statistical Methodology , 1977 , 39 ( 1 ): 1 - 22 .

DONG Y S , DING K Z , JALAIAN B , et al . AdaGNN: graph neural networks with adaptive frequency response filter [C ] // Proceedings of the 30th ACM International Conference on Information & Knowledge Management . New York : ACM Press , 2021 : 392 - 401 .

XIE J , GIRSHICK R , FARHADI A . Unsupervised deep embedding for clustering analysis [C ] // International conference on machine learning . Piscataway : IEEE Press , 2016 : 478 - 487 .

MAATEN L , POSTMA E , HERIK J . Dimensionality reduction: a comparative review [J ] . Journal of Machine Learning Research , 2009 , 10 ( 66-71 ): 13 .

MRABAH N , BOUGUESSA M , TOUATI M F , et al . Rethinking graph auto-encoder models for attributed graph clustering [J ] . IEEE Transactions on Knowledge and Data Engineering , 2023 , 35 ( 9 ): 9037 - 9053 .

ROZEMBERCZKI B , ALLEN C , SARKAR R . Multi-scale attributed node embedding [J ] . Journal of Complex Networks , 2021 , 9 ( 2 ): cnab014 .

LIU Y X , ZHENG Y , ZHANG D K , et al . Towards unsupervised deep graph structure learning [C ] // Proceedings of the ACM Web Conference 2022 . New York : ACM Press , 2022 : 1392 - 1403 .

LIU H T , LU X B , CHENG K F , et al . A multi-embedding fusion network for attributed graph clustering [J ] . Applied Soft Computing , 2024 , 165 : 112073 .

VAN D M L , HINTON G . Visualizing data using t-SNE [J ] . Journal of Machine Learning Research , 2008 , 9 ( 11 ): 2589 - 2605 .

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

暂无数据