| Autor: |
Ghadirian, M., Bigdeli, N. |
| Předmět: |
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| Zdroj: |
Scientia Iranica. Transaction D, Computer Science & Engineering & Electrical Engineering; May/Jun2023, Vol. 30 Issue 3, p1068-1084, 17p |
| Abstrakt: |
Community detection is a significant issue in extracting valuable information and understanding complex network structures. Non-negative Matrix Factorization (NMF) methods are the most remarkable topics in community detection. The Modularized trifactor NMF (Mtrinmf) method was proposed as a new class of NMF methods that combines the modularized information with tri-factor NMF. It had high computational complexity due to its dependence on the choice of the initial value of its parameter and the number of communities (c). In other words, the Mtrinmf method should search among different candidates to find correct c. In this paper, a novel Hybrid adaptive Mtrinmf (Hamtrinmf) method is proposed to improve the performance of Mtrinmf and reduce the computational complexity efficiently. In the proposed method, computational complexity reduction is made possible by selecting the right c candidates and tuning parameter. For this purpose, a hybrid algorithm including Singular Value Decomposition (SVD) and Relative Eigenvalue Gap (REG) algorithms is suggested to estimate the set of c candidates. Next, the Tuning parameter Mtrinmf (Tpmtrinmf) model is proposed to improve the performance of community detection via employing a self-tuning β parameter. Moreover, experimental results confirm the effciency of the Hamtrinmf method with respect to other reference methods on artificial and real-world networks. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Supplemental Index |
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