A new approach for estimating the number of communities in complex networks using PGD-SNMTF and GA-Reference-Cited by-同舟云学术

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A new approach for estimating the number of communities in complex networks using PGD-SNMTF and GA

Published:2023-08-09 Issue: Volume: Page:
ISSN:1868-6478
Container-title:Evolving Systems
language:en
Short-container-title:Evolving Systems

Author:

Jouyban Morteza,Hosseini Soodeh

Publisher

Springer Science and Business Media LLC

Subject

Control and Optimization,Computer Science Applications,Modeling and Simulation,Control and Systems Engineering

Link

https://link.springer.com/content/pdf/10.1007/s12530-023-09530-z.pdf

Reference43 articles.

1. Ahookhosh M, Hien LTK, Gillis N, Patrinos P (2021) A Block Inertial Bregman Proximal algorithm for nonsmooth nonconvex problems with application to symmetric nonnegative matrix tri-factorization. J Optim Theory Appl 190(1):234–258. https://doi.org/10.1007/s10957-021-01880-5

2. American football. https://www.cc.gatech.edu/dimacs10/archive/clustering.shtml

3. Asadi S, Povh J (2021) A block coordinate descent-based projected gradient algorithm for orthogonal non-negative matrix factorization. Mathematics 9(5):1–22. https://doi.org/10.3390/math9050540

4. Attea BA et al (2021) A review of heuristics and metaheuristics for community detection in complex networks: current usage, emerging development, and future directions. Swarm Evol Comput 63:100885. https://doi.org/10.1016/j.swevo.2021.100885

5. Batool F, Hennig C (2021) Clustering with the average silhouette width. Comput Stat Data Anal 158:107190. https://doi.org/10.1016/j.csda.2021.107190

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