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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.10464 |
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| _version_ | 1866909492719910912 |
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| author | Park, Minhyuk Feng, Daniel Wang Digra, Siya Vu-Le, The-Anh Chacko, George Warnow, Tandy |
| author_facet | Park, Minhyuk Feng, Daniel Wang Digra, Siya Vu-Le, The-Anh Chacko, George Warnow, Tandy |
| contents | Community detection approaches resolve complex networks into smaller groups (communities) that are expected to be relatively edge-dense and well-connected. The stochastic block model (SBM) is one of several approaches used to uncover community structure in graphs. In this study, we demonstrate that SBM software applied to various real-world and synthetic networks produces poorly-connected to disconnected clusters. We present simple modifications to improve the connectivity of SBM clusters, and show that the modifications improve accuracy using simulated networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_10464 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Improved Community Detection using Stochastic Block Models Park, Minhyuk Feng, Daniel Wang Digra, Siya Vu-Le, The-Anh Chacko, George Warnow, Tandy Social and Information Networks Community detection approaches resolve complex networks into smaller groups (communities) that are expected to be relatively edge-dense and well-connected. The stochastic block model (SBM) is one of several approaches used to uncover community structure in graphs. In this study, we demonstrate that SBM software applied to various real-world and synthetic networks produces poorly-connected to disconnected clusters. We present simple modifications to improve the connectivity of SBM clusters, and show that the modifications improve accuracy using simulated networks. |
| title | Improved Community Detection using Stochastic Block Models |
| topic | Social and Information Networks |
| url | https://arxiv.org/abs/2408.10464 |