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Improved Link Entropy with Dynamic Community Number Detection for Quantifying Significance of Edges in Complex Social Networks
Discovering communities in complex networks is essential in performing analyses, such as dynamics of political fragmentation and echo chambers in social networks. In this work, we study the problem of quantifying the significance of edges in a complex network, and propose a significantly improved ve...
Autores principales: | , , |
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Lenguaje: | eng |
Publicado: |
2023
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Materias: | |
Acceso en línea: | https://dx.doi.org/10.3390/e25020365 http://cds.cern.ch/record/2856553 |
_version_ | 1780977521603379200 |
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author | Lubashevskiy, Vasily Ozaydin, Seval Yurtcicek Ozaydin, Fatih |
author_facet | Lubashevskiy, Vasily Ozaydin, Seval Yurtcicek Ozaydin, Fatih |
author_sort | Lubashevskiy, Vasily |
collection | CERN |
description | Discovering communities in complex networks is essential in performing analyses, such as dynamics of political fragmentation and echo chambers in social networks. In this work, we study the problem of quantifying the significance of edges in a complex network, and propose a significantly improved version of the Link Entropy method. Using Louvain, Leiden and Walktrap methods, our proposal detects the number of communities in each iteration on discovering the communities. Running experiments on various benchmark networks, we show that our proposed method outperforms the Link Entropy method in quantifying edge significance. Considering also the computational complexities and possible defects, we conclude that Leiden or Louvain algorithms are the best choice for community number detection in quantifying edge significance. We also discuss designing a new algorithm for not only discovering the number of communities, but also computing the community membership uncertainties. |
id | cern-2856553 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2023 |
record_format | invenio |
spelling | cern-28565532023-04-20T20:40:08Zdoi:10.3390/e25020365http://cds.cern.ch/record/2856553engLubashevskiy, VasilyOzaydin, Seval YurtcicekOzaydin, FatihImproved Link Entropy with Dynamic Community Number Detection for Quantifying Significance of Edges in Complex Social NetworksPhysics in GeneralDiscovering communities in complex networks is essential in performing analyses, such as dynamics of political fragmentation and echo chambers in social networks. In this work, we study the problem of quantifying the significance of edges in a complex network, and propose a significantly improved version of the Link Entropy method. Using Louvain, Leiden and Walktrap methods, our proposal detects the number of communities in each iteration on discovering the communities. Running experiments on various benchmark networks, we show that our proposed method outperforms the Link Entropy method in quantifying edge significance. Considering also the computational complexities and possible defects, we conclude that Leiden or Louvain algorithms are the best choice for community number detection in quantifying edge significance. We also discuss designing a new algorithm for not only discovering the number of communities, but also computing the community membership uncertainties.oai:cds.cern.ch:28565532023 |
spellingShingle | Physics in General Lubashevskiy, Vasily Ozaydin, Seval Yurtcicek Ozaydin, Fatih Improved Link Entropy with Dynamic Community Number Detection for Quantifying Significance of Edges in Complex Social Networks |
title | Improved Link Entropy with Dynamic Community Number Detection for Quantifying Significance of Edges in Complex Social Networks |
title_full | Improved Link Entropy with Dynamic Community Number Detection for Quantifying Significance of Edges in Complex Social Networks |
title_fullStr | Improved Link Entropy with Dynamic Community Number Detection for Quantifying Significance of Edges in Complex Social Networks |
title_full_unstemmed | Improved Link Entropy with Dynamic Community Number Detection for Quantifying Significance of Edges in Complex Social Networks |
title_short | Improved Link Entropy with Dynamic Community Number Detection for Quantifying Significance of Edges in Complex Social Networks |
title_sort | improved link entropy with dynamic community number detection for quantifying significance of edges in complex social networks |
topic | Physics in General |
url | https://dx.doi.org/10.3390/e25020365 http://cds.cern.ch/record/2856553 |
work_keys_str_mv | AT lubashevskiyvasily improvedlinkentropywithdynamiccommunitynumberdetectionforquantifyingsignificanceofedgesincomplexsocialnetworks AT ozaydinsevalyurtcicek improvedlinkentropywithdynamiccommunitynumberdetectionforquantifyingsignificanceofedgesincomplexsocialnetworks AT ozaydinfatih improvedlinkentropywithdynamiccommunitynumberdetectionforquantifyingsignificanceofedgesincomplexsocialnetworks |