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Autori principali: Morea, Fabio, De Stefano, Domenico
Natura: Preprint
Pubblicazione: 2024
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Accesso online:https://arxiv.org/abs/2410.19495
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author Morea, Fabio
De Stefano, Domenico
author_facet Morea, Fabio
De Stefano, Domenico
contents This article explores the importance of examining the solution space in community detection, highlighting its role in achieving reliable results when dealing with real-world problems. A Bayesian framework is used to estimate the stability of the solution space and classify it into categories Single, Dominant, Multiple, Sparse or Empty. By applying this approach to real-world networks, the study highlights the importance of considering multiple solutions rather than relying on a single partition. This ensures more reliable results and efficient use of computational resources in community detection analysis.
format Preprint
id arxiv_https___arxiv_org_abs_2410_19495
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Beyond One Solution: The Case for a Comprehensive Exploration of Solution Space in Community Detection
Morea, Fabio
De Stefano, Domenico
Social and Information Networks
This article explores the importance of examining the solution space in community detection, highlighting its role in achieving reliable results when dealing with real-world problems. A Bayesian framework is used to estimate the stability of the solution space and classify it into categories Single, Dominant, Multiple, Sparse or Empty. By applying this approach to real-world networks, the study highlights the importance of considering multiple solutions rather than relying on a single partition. This ensures more reliable results and efficient use of computational resources in community detection analysis.
title Beyond One Solution: The Case for a Comprehensive Exploration of Solution Space in Community Detection
topic Social and Information Networks
url https://arxiv.org/abs/2410.19495