Beyond One Solution: The Case for a Comprehensive Exploration of Solution Space in Community Detection
Social and Information Networks
2026-01-21 v1
Abstract
This article explores the importance of examining the solution space in community detection, highlighting its role in achieving reliable results when dealing with real-world problems. A Bayesian framework is used to estimate the stability of the solution space and classify it into categories Single, Dominant, Multiple, Sparse or Empty. By applying this approach to real-world networks, the study highlights the importance of considering multiple solutions rather than relying on a single partition. This ensures more reliable results and efficient use of computational resources in community detection analysis.
Keywords
Cite
@article{arxiv.2410.19495,
title = {Beyond One Solution: The Case for a Comprehensive Exploration of Solution Space in Community Detection},
author = {Fabio Morea and Domenico De Stefano},
journal= {arXiv preprint arXiv:2410.19495},
year = {2026}
}