Adaptive Local Modularity Learning for Efficient Multilayer Graph Clustering-Reference-Cited by-同舟云学术

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Adaptive Local Modularity Learning for Efficient Multilayer Graph Clustering

Published:2024 Issue: Volume:72 Page:2221-2232
ISSN:1053-587X
Container-title:IEEE Transactions on Signal Processing
language:
Short-container-title:IEEE Trans. Signal Process.

Author:

Wu Danyang¹^ORCID,Wang Penglei²^ORCID,Liang Junjie³^ORCID,Lu Jitao²^ORCID,Xu Jin³^ORCID,Wang Rong⁴^ORCID,Nie Feiping²^ORCID

Affiliation:

1. College of Information Engineering, Northwest A&F University, Yangling, P. R. China

2. School of Computer Science, School of Artificial Intelligence, OPtics and ElectroNics, Northwestern Polytechnical University, Xi’an, P. R. China

3. School of Future Technology, South China University of Technology, Guangzhou, China

4. School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University, Xi’an, P. R. China

Funder

Natural Science Basic Research Plan in Shaanxi Province of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/78/10347386/10496135.pdf?arnumber=10496135

Reference60 articles.

1. Spectral and matrix factorization methods for consistent community detection in multi-layer networks

2. An overview of application of graph theory;Prathik;Int. J. Chemtech Res.,2016

3. Unsupervised feature selection via multiple graph fusion and feature weight learning

4. Dual Low-Rank Graph Autoencoder for Semantic and Topological Networks

5. Interpretable Graph Convolutional Network for Multi-View Semi-Supervised Learning

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