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

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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：2024-07-31，
  
  修回：2024-11-06，
  
  纸质出版：2025-01-20
- 稿件说明：
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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

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