可靠的节点相似性矩阵引导的对比图聚类

IF 8.9 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Yunhui Liu;Xinyi Gao;Tieke He;Tao Zheng;Jianhua Zhao;Hongzhi Yin
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引用次数: 0

摘要

图聚类涉及将图中的节点划分为互不相交的群组,对许多后续应用具有重要意义。最近,以利用监督信息著称的对比学习在深度图聚类中取得了令人鼓舞的成果。这种方法通过在表示空间内吸引正相关的节点对和拉开负相关的节点对的距离,来促进有利节点表示的学习,从而实现聚类。然而,现有方法的一个显著局限是无法深入探索节点的相似性。例如,有些方法假设表示空间内的节点相似性矩阵是相同的,从而忽略了节点之间固有的语义关系。鉴于实例相似性在聚类中的基本作用,我们的研究从节点相似性矩阵的角度研究对比图聚类。我们认为,表征空间中理想的节点相似性矩阵应能准确反映节点间固有的语义关系,确保在学习到的表征中保留语义相似性。为此,我们引入了一个新的框架--可靠的节点相似性矩阵引导的对比图聚类(NS4GC),它能估计出表征空间内近似理想的节点相似性矩阵,从而指导表征学习。我们的方法引入了节点邻接对齐和语义感知稀疏化,确保节点相似性矩阵既准确又有效稀疏。在 8 个真实世界数据集上进行的综合实验证实了学习节点相似性矩阵的有效性和 NS4GC 的卓越性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reliable Node Similarity Matrix Guided Contrastive Graph Clustering
Graph clustering, which involves the partitioning of nodes within a graph into disjoint clusters, holds significant importance for numerous subsequent applications. Recently, contrastive learning, known for utilizing supervisory information, has demonstrated encouraging results in deep graph clustering. This methodology facilitates the learning of favorable node representations for clustering by attracting positively correlated node pairs and distancing negatively correlated pairs within the representation space. Nevertheless, a significant limitation of existing methods is their inadequacy in thoroughly exploring node-wise similarity. For instance, some hypothesize that the node similarity matrix within the representation space is identical, ignoring the inherent semantic relationships among nodes. Given the fundamental role of instance similarity in clustering, our research investigates contrastive graph clustering from the perspective of the node similarity matrix. We argue that an ideal node similarity matrix within the representation space should accurately reflect the inherent semantic relationships among nodes, ensuring the preservation of semantic similarities in the learned representations. In response to this, we introduce a new framework, Reliable Node Similarity Matrix Guided Contrastive Graph Clustering (NS4GC), which estimates an approximately ideal node similarity matrix within the representation space to guide representation learning. Our method introduces node-neighbor alignment and semantic-aware sparsification, ensuring the node similarity matrix is both accurate and efficiently sparse. Comprehensive experiments conducted on 8 real-world datasets affirm the efficacy of learning the node similarity matrix and the superior performance of NS4GC.
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来源期刊
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering 工程技术-工程:电子与电气
CiteScore
11.70
自引率
3.40%
发文量
515
审稿时长
6 months
期刊介绍: The IEEE Transactions on Knowledge and Data Engineering encompasses knowledge and data engineering aspects within computer science, artificial intelligence, electrical engineering, computer engineering, and related fields. It provides an interdisciplinary platform for disseminating new developments in knowledge and data engineering and explores the practicality of these concepts in both hardware and software. Specific areas covered include knowledge-based and expert systems, AI techniques for knowledge and data management, tools, and methodologies, distributed processing, real-time systems, architectures, data management practices, database design, query languages, security, fault tolerance, statistical databases, algorithms, performance evaluation, and applications.
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