iMGC: Interactive Multiple Graph Clustering With Constrained Laplacian Rank-Reference-Cited by-同舟云学术

iMGC: Interactive Multiple Graph Clustering With Constrained Laplacian Rank

Published:2023-04 Issue:2 Volume:53 Page:427-437
ISSN:2168-2291
Container-title:IEEE Transactions on Human-Machine Systems
language:
Short-container-title:IEEE Trans. Human-Mach. Syst.

Author:

Zhou Zhiguang¹^ORCID,Sun Ling²,Wang Haoxuan²,Yu Wanghao²,Liu Yuhua¹,Zhang Xiang²,Wang Yigang¹^ORCID,Chen Wei³^ORCID

Affiliation:

1. Hangzhou Dianzi University, Hangzhou, China

2. School of Information, Zhejiang University of Finance and Economics, Hangzhou, China

3. State Key Lab of CAD & CG, Zhejiang University, Hangzhou, China

Funder

National Natural Science Foundation of China

National Statistical Science Research

Zhejiang Provincial Science and Technology Program in China

Public Welfare Plan Research Project of Zhejiang Provincial Science and Technology Department

Zhejiang Statistical Science Research Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Computer Networks and Communications,Computer Science Applications,Human-Computer Interaction,Signal Processing,Control and Systems Engineering,Human Factors and Ergonomics

Link

http://xplorestaging.ieee.org/ielx7/6221037/10075055/09999132.pdf?arnumber=9999132

Reference37 articles.

1. Finding and Characterizing Communities in Multidimensional Networks

2. Hyper-Graph Regularized Constrained NMF for Selecting Differentially Expressed Genes and Tumor Classification

3. Identification of multi-layer networks community by fusing nonnegative matrix factorization and topological structural information

4. Rank-Constrained Spectral Clustering With Flexible Embedding

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