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PGC-CSS: A parallel graph clustering framework with collaborative self-supervision

delete2025-07-03
delete0
PRE
AI
M
Meng Li
J
Jun Wu
B
Bo Yang
Y
Yuanyuan Li
屈
屈磊 (Lei Qu)
DOI：10.1016/j.knosys.2025.114010delete
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摘要

摘要

En 中文
图聚类是数据分析中的一个基础但具有挑战性的任务，旨在根据图拓扑和节点特征将节点划分为连贯的组。近年来，基于图卷积网络（GCN）的深度聚类方法取得了显著成功。然而，大多数现有方法依赖于建立在原始图结构之上的单一学习管线，而该结构常因噪声或不稳定边而被破坏。因此，聚类性能变得不稳定。为解决上述问题，我们提出了一个新颖的并行图聚类框架，称为PGC-CSS。具体而言，我们引入了一种图精炼方法，通过从节点嵌入中揭示隐含的结构模式来缓解原始图中的噪声。精炼后的图随后在并行架构中与原始图集成，通过多图结构的互补建模实现鲁棒且具有判别性的表示学习。此外，我们设计了一个协作自监督模块，结合基于分布和伪标签的自监督策略，引导模型学习最优聚类表示。在六个基准数据集上的广泛实验证明了PGC-CSS在深度属性图聚类中的有效性和优越性。

期刊

K
Knowledge-Based Systems
IF:
7.6
论文数:
1.2W
被引数:
4.5W

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