计算机应用研究2024，Vol.41Issue(6)：1769-1777,9.DOI:10.19734/j.issn.1001-3695.2023.10.0521
困难样本采样联合对比增强的深度图聚类
Deep graph clustering with hard sample sampling joint contrastive augmentation
摘要
Abstract
The graph clustering algorithm for hard samples mining is a recent research hotspot.In the current algorithm,the main problems include the lack of a fusion mechanism for comparing methods and a sample pair weighting strategy;the algorithms ignore"false negative"samples within the view when sampling positive samples and disregarding the help of graph-level informa-tion for clustering.To address the issues above,this paper proposed a graph clustering algorithm based on hard sample sampling joint contrast augmentation.Initially,it utilized an autoencoder to learn embeddings,designed a self-weighted contrast loss for representation learning by utilizing the calculated pseudo-label,similarity,and confidence information,and unified the strategies of node comparison and hard sample pair weighting across different views.By adjusting the weights of sample pairs in different confidence regions,the loss function derived the model to focus on different types of hard samples to learn discriminative fea-tures,improving the consistency of intra-cluster representation and the distinctiveness of inter-cluster representation and enhan-cing the ability to discriminate samples.Additionally,the clustering network projected the graph-level representation to maximize the representation consistency of clusters under different views through cluster contrast loss.Finally,combining the two compari-son losses,the self-supervised training is used for iterative optimization to complete clustering.In the comparison with 9 bench-mark algorithms on 5 real datasets,this algorithm achieves superior performance on 4 authoritative indicators,highlighting its ex-cellent clustering capabilities.Ablation experiments demonstrate the effectiveness and transferability of the two contrasting mod-ules.关键词
图表示学习/属性图聚类/对比学习/困难样本挖掘Key words
graph representation learning/attributed graph clustering/contrastive learning/hard sample mining分类
信息技术与安全科学引用本文复制引用
朱玄烨,孔兵,陈红梅,包崇明,周丽华..困难样本采样联合对比增强的深度图聚类[J].计算机应用研究,2024,41(6):1769-1777,9.基金项目
国家自然科学基金资助项目(62062066,61762090,61966036,62276227) （62062066,61762090,61966036,62276227）
2022年云南省基础研究计划重点项目(202201AS070015) （202201AS070015）
云南省中青年学术和技术带头人后备人才资助项目(202205AC160033) （202205AC160033）
云南省智能系统与计算重点实验室资助项目(202205AG070003) （202205AG070003）
