{"ID":2879134,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.17013","arxiv_id":"2508.17013","title":"Dense Subgraph Clustering and a New Cluster Ensemble Method","abstract":"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.","short_abstract":"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 h...","url_abs":"https://arxiv.org/abs/2508.17013","url_pdf":"https://arxiv.org/pdf/2508.17013v2","authors":"[\"The-Anh Vu-Le\",\"João Alfredo Cardoso Lamy\",\"Tomás Alessi\",\"Ian Chen\",\"Minhyuk Park\",\"Elfarouk Harb\",\"George Chacko\",\"Tandy Warnow\"]","published":"2025-08-23T12:53:55Z","proceeding":"cs.SI","tasks":"[\"cs.SI\"]","methods":"[]","has_code":false}