|تعداد مشاهده مقاله||2,363,615|
|تعداد دریافت فایل اصل مقاله||1,661,773|
A New Hybrid NMF-based Infrastructure for Community Detection in Complex Networks
|Journal of Electrical and Computer Engineering Innovations (JECEI)|
|مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 17 اردیبهشت 1402|
|نوع مقاله: Original Research Paper|
|شناسه دیجیتال (DOI): 10.22061/jecei.2023.9150.577|
|M. Ghadirian؛ N. Bigdeli*|
|Department of Control Engineering, Faculty of Technical and Engineering, Imam-Khomeini International University, Qazvin, Iran.|
|تاریخ دریافت: 19 دی 1401، تاریخ بازنگری: 01 اردیبهشت 1402، تاریخ پذیرش: 17 اردیبهشت 1402|
|Background and Objectives: Community detection is a critical problem in investigating complex networks. Community detection based on modularity/general modularity density are the popular methods with the advantage of using complex network features and the disadvantage of being NP-hard problem for clustering. Moreover, Non-negative matrix factorization (NMF)-based community detection methods are a family of community detection tools that utilize network topology; but most of them cannot thoroughly exploit network features. In this paper, a hybrid NMF-based community detection infrastructure is developed, including modularity/ general modularity density as more comprehensive indices of networks. The proposed infrastructure enables to solve the challenges of combining the NMF method with modularity/general modularity density criteria and improves the community detection methods for complex networks.|
Methods: First, new representations, similar to the model of symmetric NMF, are derived for the model of community detection based on modularity/general modularity density. Next, these indices are innovatively augmented to the proposed hybrid NMF-based model as two novel models called ‘general modularity density NMF (GMDNMF) and mixed modularity NMF (MMNMF)’. In order to solve these two NP-hard problems, two iterative optimization algorithms are developed.
Results: it is proved that the modularity/general modularity density-based community detection can be consistently represented in the form of SNMF-based community detection. The performances of the proposed models are verified on various artificial and real-world networks of different sizes. It is shown that MMNMF and GMDNMF models outperform other community detection methods. Moreover, the GMDNMF model has better performance with higher computational complexity compared to the MMNMF model.
Conclusion: The results show that the proposed MMNMF model improves the performance of community detection based on NMF by employing the modularity index as a network feature for the NMF model, and the proposed GMDNMF model enhances NMF-based community detection by using the general modularity density index.
|Complex Networks؛ Nonnegative Matrix Factorization؛ Modularity؛ General Modularity Density؛ Graph Clustering|
تعداد مشاهده مقاله: 19