Integrating topology and content equally in non-negative matrix factorization for community detection-Reference-Cited by-同舟云学术

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Integrating topology and content equally in non-negative matrix factorization for community detection

Published:2024-12 Issue: Volume:255 Page:124713
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Luo Ge^ORCID,Zhao Zhili^ORCID,Liu Shifa^ORCID,Wu Simin,Hu Ahui,Zhang Nana

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Natural Science Foundation of Gansu Province

National College Students Innovation and Entrepreneurship Training Program

Publisher

Elsevier BV

Reference52 articles.

1. Community detection in networks through a deep robust auto-encoder nonnegative matrix factorization;Al-sharoa;Engineering Applications of Artificial Intelligence,2023

2. Detecting network communities by propagating labels under constraints;Barber;Physical Review E,2009

3. Quantum semi non-negative matrix factorization;Benlamine,2021

4. Collaborative learning to improve the non-uniqueness of NMF;Benlamine;International Journal of Computational Intelligence and Applications,2022

5. WSNMF: Weighted symmetric nonnegative matrix factorization for attributed graph clustering;Berahmand;Neurocomputing,2024

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