Enhancing Community Detection in Networks: A Comparative Analysis of Local Metrics and Hierarchical Algorithms

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Bibliographic Details
Main Authors:	Palacio-Niño, Julio-Omar, Berzal, Fernando
Format:	Preprint
Published:	2024
Subjects:	Social and Information Networks Machine Learning
Online Access:	Acceder al recurso
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author	Palacio-Niño, Julio-Omar Berzal, Fernando
author_facet	Palacio-Niño, Julio-Omar Berzal, Fernando
contents	The analysis and detection of communities in network structures are becoming increasingly relevant for understanding social behavior. One of the principal challenges in this field is the complexity of existing algorithms. The Girvan-Newman algorithm, which uses the betweenness metric as a measure of node similarity, is one of the most representative algorithms in this area. This study employs the same method to evaluate the relevance of using local similarity metrics for community detection. A series of local metrics were tested on a set of networks constructed using the Girvan-Newman basic algorithm. The efficacy of these metrics was evaluated by applying the base algorithm to several real networks with varying community sizes, using modularity and NMI. The results indicate that approaches based on local similarity metrics have significant potential for community detection.
format	Preprint
id	arxiv_https___arxiv_org_abs_2408_09072
institution	arXiv
publishDate	2024
record_format	arxiv
spellingShingle	Enhancing Community Detection in Networks: A Comparative Analysis of Local Metrics and Hierarchical Algorithms Palacio-Niño, Julio-Omar Berzal, Fernando Social and Information Networks Machine Learning The analysis and detection of communities in network structures are becoming increasingly relevant for understanding social behavior. One of the principal challenges in this field is the complexity of existing algorithms. The Girvan-Newman algorithm, which uses the betweenness metric as a measure of node similarity, is one of the most representative algorithms in this area. This study employs the same method to evaluate the relevance of using local similarity metrics for community detection. A series of local metrics were tested on a set of networks constructed using the Girvan-Newman basic algorithm. The efficacy of these metrics was evaluated by applying the base algorithm to several real networks with varying community sizes, using modularity and NMI. The results indicate that approaches based on local similarity metrics have significant potential for community detection.
title	Enhancing Community Detection in Networks: A Comparative Analysis of Local Metrics and Hierarchical Algorithms
topic	Social and Information Networks Machine Learning
url	https://arxiv.org/abs/2408.09072

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