Dense Subgraph Clustering and a New Cluster Ensemble Method
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915470661124096 |
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| author | Vu-Le, The-Anh Lamy, João Alfredo Cardoso Alessi, Tomás Chen, Ian Park, Minhyuk Harb, Elfarouk Chacko, George Warnow, Tandy |
| author_facet | Vu-Le, The-Anh Lamy, João Alfredo Cardoso Alessi, Tomás Chen, Ian Park, Minhyuk Harb, Elfarouk Chacko, George Warnow, Tandy |
| contents | We propose DSC-Flow-Iter, a new community detection algorithm that is based on iterative extraction of dense subgraphs. Although DSC-Flow-Iter leaves many nodes unclustered, it is competitive with leading methods and has high-precision and low-recall, making it complementary to modularity-based methods that typically have high recall but lower precision. Based on this observation, we introduce a novel cluster ensemble technique that combines DSC-Flow-Iter with modularity-based clustering, to provide improved accuracy. We show that our proposed pipeline, which uses this ensemble technique, outperforms its individual components and improves upon the baseline techniques on a large collection of synthetic networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_17013 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Dense Subgraph Clustering and a New Cluster Ensemble Method Vu-Le, The-Anh Lamy, João Alfredo Cardoso Alessi, Tomás Chen, Ian Park, Minhyuk Harb, Elfarouk Chacko, George Warnow, Tandy Social and Information Networks We propose DSC-Flow-Iter, a new community detection algorithm that is based on iterative extraction of dense subgraphs. Although DSC-Flow-Iter leaves many nodes unclustered, it is competitive with leading methods and has high-precision and low-recall, making it complementary to modularity-based methods that typically have high recall but lower precision. Based on this observation, we introduce a novel cluster ensemble technique that combines DSC-Flow-Iter with modularity-based clustering, to provide improved accuracy. We show that our proposed pipeline, which uses this ensemble technique, outperforms its individual components and improves upon the baseline techniques on a large collection of synthetic networks. |
| title | Dense Subgraph Clustering and a New Cluster Ensemble Method |
| topic | Social and Information Networks |
| url | https://arxiv.org/abs/2508.17013 |