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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...

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Autores principales: Lubashevskiy, Vasily, Ozaydin, Seval Yurtcicek, Ozaydin, Fatih
Lenguaje:eng
Publicado: 2023
Materias:
Acceso en línea:https://dx.doi.org/10.3390/e25020365
http://cds.cern.ch/record/2856553
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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.
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institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2023
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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