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Extracting backbones in weighted modular complex networks
Network science provides effective tools to model and analyze complex systems. However, the increasing size of real-world networks becomes a major hurdle in order to understand their structure and topological features. Therefore, mapping the original network into a smaller one while preserving its i...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7511995/ https://www.ncbi.nlm.nih.gov/pubmed/32968081 http://dx.doi.org/10.1038/s41598-020-71876-0 |
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author | Ghalmane, Zakariya Cherifi, Chantal Cherifi, Hocine El Hassouni, Mohammed |
author_facet | Ghalmane, Zakariya Cherifi, Chantal Cherifi, Hocine El Hassouni, Mohammed |
author_sort | Ghalmane, Zakariya |
collection | PubMed |
description | Network science provides effective tools to model and analyze complex systems. However, the increasing size of real-world networks becomes a major hurdle in order to understand their structure and topological features. Therefore, mapping the original network into a smaller one while preserving its information is an important issue. Extracting the so-called backbone of a network is a very challenging problem that is generally handled either by coarse-graining or filter-based methods. Coarse-graining methods reduce the network size by grouping similar nodes, while filter-based methods prune the network by discarding nodes or edges based on a statistical property. In this paper, we propose and investigate two filter-based methods exploiting the overlapping community structure in order to extract the backbone in weighted networks. Indeed, highly connected nodes (hubs) and overlapping nodes are at the heart of the network. In the first method, called “overlapping nodes ego backbone”, the backbone is formed simply from the set of overlapping nodes and their neighbors. In the second method, called “overlapping nodes and hubs backbone”, the backbone is formed from the set of overlapping nodes and the hubs. For both methods, the links with the lowest weights are removed from the network as long as a backbone with a single connected component is preserved. Experiments have been performed on real-world weighted networks originating from various domains (social, co-appearance, collaboration, biological, and technological) and different sizes. Results show that both backbone extraction methods are quite similar. Furthermore, comparison with the most influential alternative filtering method demonstrates the greater ability of the proposed backbones extraction methods to uncover the most relevant parts of the network. |
format | Online Article Text |
id | pubmed-7511995 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-75119952020-09-29 Extracting backbones in weighted modular complex networks Ghalmane, Zakariya Cherifi, Chantal Cherifi, Hocine El Hassouni, Mohammed Sci Rep Article Network science provides effective tools to model and analyze complex systems. However, the increasing size of real-world networks becomes a major hurdle in order to understand their structure and topological features. Therefore, mapping the original network into a smaller one while preserving its information is an important issue. Extracting the so-called backbone of a network is a very challenging problem that is generally handled either by coarse-graining or filter-based methods. Coarse-graining methods reduce the network size by grouping similar nodes, while filter-based methods prune the network by discarding nodes or edges based on a statistical property. In this paper, we propose and investigate two filter-based methods exploiting the overlapping community structure in order to extract the backbone in weighted networks. Indeed, highly connected nodes (hubs) and overlapping nodes are at the heart of the network. In the first method, called “overlapping nodes ego backbone”, the backbone is formed simply from the set of overlapping nodes and their neighbors. In the second method, called “overlapping nodes and hubs backbone”, the backbone is formed from the set of overlapping nodes and the hubs. For both methods, the links with the lowest weights are removed from the network as long as a backbone with a single connected component is preserved. Experiments have been performed on real-world weighted networks originating from various domains (social, co-appearance, collaboration, biological, and technological) and different sizes. Results show that both backbone extraction methods are quite similar. Furthermore, comparison with the most influential alternative filtering method demonstrates the greater ability of the proposed backbones extraction methods to uncover the most relevant parts of the network. Nature Publishing Group UK 2020-09-23 /pmc/articles/PMC7511995/ /pubmed/32968081 http://dx.doi.org/10.1038/s41598-020-71876-0 Text en © The Author(s) 2020 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Ghalmane, Zakariya Cherifi, Chantal Cherifi, Hocine El Hassouni, Mohammed Extracting backbones in weighted modular complex networks |
title | Extracting backbones in weighted modular complex networks |
title_full | Extracting backbones in weighted modular complex networks |
title_fullStr | Extracting backbones in weighted modular complex networks |
title_full_unstemmed | Extracting backbones in weighted modular complex networks |
title_short | Extracting backbones in weighted modular complex networks |
title_sort | extracting backbones in weighted modular complex networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7511995/ https://www.ncbi.nlm.nih.gov/pubmed/32968081 http://dx.doi.org/10.1038/s41598-020-71876-0 |
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