Journal article
JANE: Just Another latent space NEtwork clustering algorithm
Computational statistics & data analysis, Vol.211, 108228
11/2025
DOI: 10.1016/j.csda.2025.108228
Abstract
While latent space network models have been a popular approach for community detection for over 15 years, major computational challenges remain, limiting the ability to scale beyond small networks. The R statistical software package, JANE, introduces a new estimation algorithm with massive speedups derived from: (1) a low dimensional approximation approach to adjust for degree heterogeneity parameters; (2) an approximation of intractable likelihood terms; (3) a fast initialization algorithm; and (4) a novel set of convergence criteria focused on clustering performance. Additionally, the proposed method addresses limitations of current implementations, which rely on a restrictive spherical-shape assumption for the prior distribution on the latent positions; relaxing this constraint allows for greater flexibility across diverse network structures. A simulation study evaluating clustering performance of the proposed approach against state-of-the-art methods shows dramatically improved clustering performance in most scenarios and significant reductions in computational time — up to 45 times faster compared to existing approaches.
Details
- Title: Subtitle
- JANE: Just Another latent space NEtwork clustering algorithm
- Creators
- Alan T. Arakkal - University of IowaDaniel K. Sewell - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Computational statistics & data analysis, Vol.211, 108228
- Publisher
- Elsevier B.V
- DOI
- 10.1016/j.csda.2025.108228
- ISSN
- 0167-9473
- eISSN
- 1872-7352
- Language
- English
- Electronic publication date
- 06/02/2025
- Date published
- 11/2025
- Academic Unit
- Biostatistics
- Record Identifier
- 9984829023102771
Metrics
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