计算机科学
聚类分析
人工智能
平滑的
数据挖掘
图形
机器学习
邻接表
深度学习
特征学习
邻接矩阵
模式识别(心理学)
理论计算机科学
算法
计算机视觉
作者
Bowei Chen,Sen Xu,Hui Xu,Xuesheng Bian,Naixuan Guo,Xin-Shun Xu,Xiaopeng Hua
标识
DOI:10.1016/j.neunet.2023.08.020
摘要
Recently, deep clustering has been extensively employed for various data mining tasks, and it can be divided into auto-encoder (AE)-based and graph neural networks (GNN)-based methods. However, existing AE-based methods fall short in effectively extracting structural information, while GNN suffer from smoothing and heterophily. Although methods that combine AE and GNN achieve impressive performance, there remains an inadequate balance between preserving the raw structure and exploring the underlying structure. Accordingly, we propose a novel network named Structure-Aware Deep Clustering network (SADC). Firstly, we compute the cumulative influence of non-adjacent nodes at multiple depths and, thus, enhance the adjacency matrix. Secondly, an enhanced graph auto-encoder is designed. Thirdly, the latent space of AE is endowed with the ability to perceive the raw structure during the learning process. Besides, we design self-supervised mechanisms to achieve co-optimization of node representation learning and topology learning. A new loss function is designed to preserve the inherent structure while also allowing for exploration of latent data structure. Extensive experiments on six benchmark datasets validate that our method outperforms state-of-the-art methods.
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