A New Hybrid NMF-based Infrastructure for Community Detection in ‎Complex Networks

Author(s):
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Article Type:
Research/Original Article (دارای رتبه معتبر)
Abstract:
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.‎
Language:
English
Published:
Journal of Electrical and Computer Engineering Innovations, Volume:11 Issue: 2, Summer-Autumn 2023
Pages:
443 to 458
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