SignedLouvain: Louvain for signed networks
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866916338194186240 |
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| author | Pougué-Biyong, John N. Lambiotte, Renaud |
| author_facet | Pougué-Biyong, John N. Lambiotte, Renaud |
| contents | In this article, we consider the problem of community detection in signed networks. We propose SignedLouvain, an adaptation of the Louvain method to maximise signed modularity, efficiently taking advantage of the structure induced by signed relations. We begin by identifying the inherent limitations of applying the standard Louvain algorithm to signed networks, before introducing a novel variant specifically engineered to overcome these challenges. Through extensive experiments on real-world datasets, we demonstrate that the proposed method not only maintains the speed and scalability of its predecessor but also significantly enhances accuracy in detecting communities within signed networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_19288 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SignedLouvain: Louvain for signed networks Pougué-Biyong, John N. Lambiotte, Renaud Social and Information Networks Data Analysis, Statistics and Probability Physics and Society In this article, we consider the problem of community detection in signed networks. We propose SignedLouvain, an adaptation of the Louvain method to maximise signed modularity, efficiently taking advantage of the structure induced by signed relations. We begin by identifying the inherent limitations of applying the standard Louvain algorithm to signed networks, before introducing a novel variant specifically engineered to overcome these challenges. Through extensive experiments on real-world datasets, we demonstrate that the proposed method not only maintains the speed and scalability of its predecessor but also significantly enhances accuracy in detecting communities within signed networks. |
| title | SignedLouvain: Louvain for signed networks |
| topic | Social and Information Networks Data Analysis, Statistics and Probability Physics and Society |
| url | https://arxiv.org/abs/2407.19288 |