Dense Subgraph Clustering and a New Cluster Ensemble Method
Social and Information Networks
2025-09-03 v2
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.
Cite
@article{arxiv.2508.17013,
title = {Dense Subgraph Clustering and a New Cluster Ensemble Method},
author = {The-Anh Vu-Le and João Alfredo Cardoso Lamy and Tomás Alessi and Ian Chen and Minhyuk Park and Elfarouk Harb and George Chacko and Tandy Warnow},
journal= {arXiv preprint arXiv:2508.17013},
year = {2025}
}