基于无负采样和权重去相关对比网络的图无关聚类

doi:10.16451/j.cnki.issn1003-6059.202510001

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Abstract Existing contrastive deep graph clustering methods heavily rely on the homophily assumption of the input graph, and thereby result in false negatives in negative sampling and feature redundancy in heterophilous graphs. These problems degrade clustering performance. To solve these problems, a graph-agnostic clustering framework, negative-free and weight-decorrelated contrastive network（NFWD）, is proposed. First, a feature graph is constructed as a supplementary view by node attribute similarity. Node representations from the feature graph and the original graph are obtained via Laplacian smoothing filters and a shared-parameter multilayer perceptron, respectively. Consequently, the reliance of the original graph on the homophily assumption is significantly reduced. Second, to tackle the false negative problem caused by class conflicts in heterophilous graphs, cluster information is derived from adaptively fused node representations for the construction of cluster-central node representations. Then, a negative-free strategy combining node-level and cluster-level feature contrast is proposed to effectively mitigate this problem. Finally, an orthogonal constraint is applied to the weight matrix of MLP to actively suppress redundant features. Experiments on six benchmark graph datasets demonstrate the effectiveness and robustness of NFWD in graph-agnostic scenarios.

Key words： Graph-Agnostic Clustering Node Similarity Weight Decorrelation Contrastive Learning

Received: 13 October 2025

TP 391

Fund:National Natural Science Foundation of China（No.62120106008）, Anhui Provincial Science and Technology Fortification Plan（No.202423k09020015）, Open Project of the Key Laboratory of Knowledge Engineering with Big Data of the Ministry of Education（No.BigKEOpen2025-05）

Corresponding Authors: WU Xindong, Ph.D., professor. His research interests include data mining, big data analysis, and knowledge-based systems.

About author:: BAI Shengxing, Ph.D.candidate. His research interests include data mining.ZHOU Peng, Ph.D., associate professor. His research interests include data mining and machine learning.ZHANG Yuhong, Ph.D., associate professor. Her research interests include data mi-ning and machine learning.

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Cite this article:

BAI Shengxing,ZHANG Yuhong,ZHOU Peng等. Negative-Free and Weight-Decorrelated Contrastive Network for Graph-Agnostic Clustering[J]. Pattern Recognition and Artificial Intelligence, 2025, 38(10): 861-875.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202510001 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2025/V38/I10/861

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