An enforced non-negative matrix factorization based approach towards community detection in dynamic networks
Identifying community structures within network dynamics is important for analysing the latent structure of the network, understanding the functions of the network, predicting the evolution of the network as well as detecting unusual events of the network. From various perspectives, a diversity of a...
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| Format: | Article |
| Language: | English |
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Saint Petersburg National Research University of Information Technologies, Mechanics and Optics (ITMO University)
2022-10-01
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| Series: | Naučno-tehničeskij Vestnik Informacionnyh Tehnologij, Mehaniki i Optiki |
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| Online Access: | https://ntv.ifmo.ru/file/article/21519.pdf |
| _version_ | 1811234699172904960 |
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| author | Shafia Bashir Manzoor Ahmad Chachoo |
| author_facet | Shafia Bashir Manzoor Ahmad Chachoo |
| author_sort | Shafia Bashir |
| collection | DOAJ |
| description | Identifying community structures within network dynamics is important for analysing the latent structure of the network, understanding the functions of the network, predicting the evolution of the network as well as detecting unusual events of the network. From various perspectives, a diversity of approaches towards dynamic community detection has been advised. However, owing to the difficulty in parameter adjustment, high temporal complexity and detection accuracy is diminishing as time slice rises; and recognizing the community composition in dynamic networks gets extremely complex. The basic models, principles, qualities, and techniques of latent factor models, as well as their various modifications, generalizations and extensions, are summed up systematically in this study which focuses on both theoretical and experimental research into latent factor models across the latest ten years. Latent factor model
like non-negative matrix factorization is considered one of the most successful models for community identification which aims to uncover distributed lower dimension representation so as to reveal community node membership. These models are mostly centred on reconstructing the network from node representations while requiring the representation to have special desirable qualities (non-negativity). The purpose of this work is to provide an experimental as well as theoretical comparative analysis of the latent factor approaches employed to detect communities within dynamic networks. Parallelly we have devised the generic and improved non-negative matrix factorization-based model which will help in producing robust community detection results in dynamic networks. The results have been calculated from the experiments done in Python. Moreover our models methodology focuses on information dynamics so as to quantify the information propagation among the involved nodes unlike existing methods that considers networks first-order topological information described by its adjacency matrix without considering the information propagation between
the nodes. In addition, this paper intends to create a unified, state of the art framework meant for non-negative matrix factorization conception which could be useful for future study. |
| first_indexed | 2024-04-12T11:40:07Z |
| format | Article |
| id | doaj.art-ba03e609e0524185ae0c26b3390bf064 |
| institution | Directory Open Access Journal |
| issn | 2226-1494 2500-0373 |
| language | English |
| last_indexed | 2024-04-12T11:40:07Z |
| publishDate | 2022-10-01 |
| publisher | Saint Petersburg National Research University of Information Technologies, Mechanics and Optics (ITMO University) |
| record_format | Article |
| series | Naučno-tehničeskij Vestnik Informacionnyh Tehnologij, Mehaniki i Optiki |
| spelling | doaj.art-ba03e609e0524185ae0c26b3390bf0642022-12-22T03:34:40ZengSaint Petersburg National Research University of Information Technologies, Mechanics and Optics (ITMO University)Naučno-tehničeskij Vestnik Informacionnyh Tehnologij, Mehaniki i Optiki2226-14942500-03732022-10-0122594195010.17586/2226-1494-2022-22-5-941-950An enforced non-negative matrix factorization based approach towards community detection in dynamic networksShafia Bashir0https://orcid.org/0000-0002-5570-967XManzoor Ahmad Chachoo1https://orcid.org/0000-0001-6702-6633Research Scholar, University of Kashmir, Srinagar, 190006, India, sc 57210047446PhD, Scientist-D, University of Kashmir, Srinagar, 190006, India, sc 56252797100Identifying community structures within network dynamics is important for analysing the latent structure of the network, understanding the functions of the network, predicting the evolution of the network as well as detecting unusual events of the network. From various perspectives, a diversity of approaches towards dynamic community detection has been advised. However, owing to the difficulty in parameter adjustment, high temporal complexity and detection accuracy is diminishing as time slice rises; and recognizing the community composition in dynamic networks gets extremely complex. The basic models, principles, qualities, and techniques of latent factor models, as well as their various modifications, generalizations and extensions, are summed up systematically in this study which focuses on both theoretical and experimental research into latent factor models across the latest ten years. Latent factor model like non-negative matrix factorization is considered one of the most successful models for community identification which aims to uncover distributed lower dimension representation so as to reveal community node membership. These models are mostly centred on reconstructing the network from node representations while requiring the representation to have special desirable qualities (non-negativity). The purpose of this work is to provide an experimental as well as theoretical comparative analysis of the latent factor approaches employed to detect communities within dynamic networks. Parallelly we have devised the generic and improved non-negative matrix factorization-based model which will help in producing robust community detection results in dynamic networks. The results have been calculated from the experiments done in Python. Moreover our models methodology focuses on information dynamics so as to quantify the information propagation among the involved nodes unlike existing methods that considers networks first-order topological information described by its adjacency matrix without considering the information propagation between the nodes. In addition, this paper intends to create a unified, state of the art framework meant for non-negative matrix factorization conception which could be useful for future study.https://ntv.ifmo.ru/file/article/21519.pdfcommunity detectionprincipal component analysisorthogonalitynon-negative matrix factorizationsingular value decompositionsocial network analysis |
| spellingShingle | Shafia Bashir Manzoor Ahmad Chachoo An enforced non-negative matrix factorization based approach towards community detection in dynamic networks Naučno-tehničeskij Vestnik Informacionnyh Tehnologij, Mehaniki i Optiki community detection principal component analysis orthogonality non-negative matrix factorization singular value decomposition social network analysis |
| title | An enforced non-negative matrix factorization based approach towards community detection in dynamic networks |
| title_full | An enforced non-negative matrix factorization based approach towards community detection in dynamic networks |
| title_fullStr | An enforced non-negative matrix factorization based approach towards community detection in dynamic networks |
| title_full_unstemmed | An enforced non-negative matrix factorization based approach towards community detection in dynamic networks |
| title_short | An enforced non-negative matrix factorization based approach towards community detection in dynamic networks |
| title_sort | enforced non negative matrix factorization based approach towards community detection in dynamic networks |
| topic | community detection principal component analysis orthogonality non-negative matrix factorization singular value decomposition social network analysis |
| url | https://ntv.ifmo.ru/file/article/21519.pdf |
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