计算机科学
聚类分析
图形
理论计算机科学
冗余(工程)
人工智能
数据挖掘
机器学习
模式识别(心理学)
操作系统
作者
Siyu Yi,Wei Ju,Yifang Qin,Xiao Luo,Luchen Liu,Yixiao Zhou,Ming Zhang
出处
期刊:IEEE transactions on neural networks and learning systems
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:: 1-15
标识
DOI:10.1109/tnnls.2023.3314451
摘要
Graph clustering, which learns the node representations for effective cluster assignments, is a fundamental yet challenging task in data analysis and has received considerable attention accompanied by graph neural networks (GNNs) in recent years. However, most existing methods overlook the inherent relational information among the nonindependent and nonidentically distributed nodes in a graph. Due to the lack of exploration of relational attributes, the semantic information of the graph-structured data fails to be fully exploited which leads to poor clustering performance. In this article, we propose a novel self-supervised deep graph clustering method named relational redundancy-free graph clustering (R $^2$ FGC) to tackle the problem. It extracts the attribute-and structure-level relational information from both global and local views based on an autoencoder (AE) and a graph AE (GAE). To obtain effective representations of the semantic information, we preserve the consistent relationship among augmented nodes, whereas the redundant relationship is further reduced for learning discriminative embeddings. In addition, a simple yet valid strategy is used to alleviate the oversmoothing issue. Extensive experiments are performed on widely used benchmark datasets to validate the superiority of our R $^2$ FGC over state-of-the-art baselines. Our codes are available at https://github.com/yisiyu95/R2FGC.
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