基于对比共识图学习的多视图属性图聚类算法  

Multi-view Attributed Graph Clustering Based on Contrast Consensus Graph Learning

作　　者：刘鹏仪 胡节 王红军[1,2,3,4] 彭博[1,2,3,4] LIU Pengyi;HU Jie;WANG Hongjun;PENG Bo(School of Computing and Artificial Intelligence,Southwest Jiaotong University,Chengdu 611756,China;Engineering Research Center of Sustainable Urban Intelligent Transportation,Ministry of Education,Chengdu 611756,China;National Engineering Laboratory of Integrated Transportation Big Data Application Technology,Chengdu 611756,China;Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province,Southwest Jiaotong University,Chengdu 611756,China)

机构地区：[1]西南交通大学计算机与人工智能学院,成都611756 [2]教育部城市智能交通工程研究中心,成都611756 [3]西南交通大学综合交通大数据应用技术国家工程实验室,成都611756 [4]西南交通大学四川省制造业产业链协同与信息支撑技术重点实验室,成都611756

出　　处：《计算机科学》2024年第11期73-80,共8页Computer Science

基　　金：国家自然科学基金(62276216);四川省重点研发项目(2023YFG0354);2023年西南交通大学国际学生教育管理研究项目(23LXSGL01)。

摘　　要：多视图属性图聚类可以将具有多个视图的图数据的节点划分到不同的簇中,近年来受到了研究者的广泛关注。目前,已有许多基于图神经网络的多视图属性图聚类方法被提出并取得了较好的聚类性能。然而,由于图神经网络难以处理数据收集过程中出现的图噪声,因此基于图神经网络的多视图属性图方法很难进一步提高聚类性能。为此,提出了一种新的基于对比共识图学习的多视图属性图聚类算法,以降低噪声对聚类的影响从而得到更好的结果。该算法包括4个步骤:首先,使用图滤波消除图上的噪声,并同时保留完整的图结构;然后,选择少量节点来学习共识图,以降低计算复杂度;随后,使用图对比正则化来帮助学习共识图;最后,利用谱聚类获得聚类结果。大量的实验结果表明,与当前最先进的方法相比,所提算法能够很好地减少图数据中噪声对聚类的影响,并以较高的执行效率取得良好的聚类结果。Multi-view attribute graph clustering can divide nodes of graph data with multiple views into different clusters,which has attracted widespread attention from researchers in recent years.At present,many multi-view attribute graph clustering me-thods based on graph neural networks have been proposed and achieved considerable clustering performance.However,since graph neural networks are difficult to deal with graph noise that occurs during data collection,it is difficult for multi-view attri-bute graph methods based on graph neural networks to further improve clustering performance.Therefore,a new multi-view attribute graph clustering method based on contrastive consensus graph learning is proposed to reduce the impact of noise on clustering and obtain better results.This method consists of four steps.First,graph filtering is used to remove noise on the graph while retaining the intact graph structure.Then,a small number of nodes are selected to learn the consensus graph to reduce computational complexity.Subsequently,graph contrast regularization is used to help learn the consensus graph.Finally,spectral clustering is used to obtain clustering results.A large number of experimental results show that compared with the current state-of-the-art methods,the proposed method can well reduce the impact of noise in graph data on clustering and achieve considerable clustering results with fast execution efficiency.

关 键 词：多视图学习 属性图数据 图聚类 对比共识图学习 图过滤 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]