嵌入
计算机科学
图嵌入
聚类分析
子空间拓扑
线性子空间
群落结构
理论计算机科学
噪音(视频)
约束(计算机辅助设计)
图形
人工智能
数据挖掘
拓扑(电路)
数学
几何学
组合数学
图像(数学)
作者
Zhongjing Yu,Gangyi Zhang,Jingyu Chen,Haoran Chen,Duo Zhang,Qinli Yang,Junming Shao
出处
期刊:IEEE transactions on cybernetics
[Institute of Electrical and Electronics Engineers]
日期:2023-05-01
卷期号:53 (5): 2980-2992
被引量:1
标识
DOI:10.1109/tcyb.2021.3124274
摘要
Most existing approaches of attributed network embedding often combine topology and attribute information based on the homophily assumption. In many real-world networks, such an assumption does not hold since the nodes are usually associated with many noisy or irrelevant attributes. To tackle this issue, we propose a noise-resistant graph embedding method, called NGE, by leveraging the subspace clustering information (i.e., the formation of communities is driven by different latent features in distinct subspaces). Specifically, we first construct a tensor to represent a given attributed network and then map it into different feature subspaces to capture community structure via tensor decomposition. For structure embedding, the link-level and community-level constraints are imposed. For attribute embedding, the feature-selection constraint is used to reinforce the relationship between topology and noise-removal attributes. By learning structure and attribute embedding with subspace clustering information, NGE can benefit both community detection, link prediction, and node classification. Extensive experimental results have demonstrated the superiority of NGE over many state-of-the-art approaches.
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