2007/09/30 by Jake M. Hofman, Chris H. Wiggins
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · Physics and Astronomy · #Advanced Graph Neural Networks #Bioinformatics and Genomic Networks #Complex Network Analysis Techniques #cond-mat.stat-mech #physics.data-an #stat.ML
paper · pdf · doi:10.1103/physrevlett.100.258701
published as Phys. Rev. Lett. 100, 258701 (2008) · Phys. Rev. Lett. 100, 258701 (2008)
arxiv created 2008/06/23 · openalex publication_date 2008/06/23 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
We present an efficient, principled, and interpretable technique for inferring module assignments and for identifying the optimal number of modules in a given network. We show how several existing methods for finding modules can be described as variant, special, or limiting cases of our work, and how the method overcomes the resolution limit problem, accurately recovering the true number of modules. Our approach is based on Bayesian methods for model selection which have been used with success for almost a century, implemented using a variational technique developed only in the past decade. We apply the technique to synthetic and real networks and outline how the method naturally allows selection among competing models.