Journal article
Automated detection of edge clusters via an overfitted mixture prior
Network science (Cambridge University Press), Vol.12(1), pp.88-106
03/2024
DOI: 10.1017/nws.2023.22
Appears in UI Libraries Support Open Access
Abstract
Abstract Most community detection methods focus on clustering actors with common features in a network. However, clustering edges offers a more intuitive way to understand the network structure in many real-life applications. Among the existing methods for network edge clustering, the majority are algorithmic, with the exception of the latent space edge clustering (LSEC) model proposed by Sewell ( Journal of Computational and Graphical Statistics, 30 (2), 390–405, 2021). LSEC was shown to have good performance in simulation and real-life data analysis, but fitting this model requires prior knowledge of the number of clusters and latent dimensions, which are often unknown to researchers. Within a Bayesian framework, we propose an extension to the LSEC model using a sparse finite mixture prior that supports automated selection of the number of clusters. We refer to our proposed approach as the automated LSEC or aLSEC. We develop a variational Bayes generalized expectation-maximization approach and a Hamiltonian Monte Carlo-within Gibbs algorithm for estimation. Our simulation study showed that aLSEC reduced run time by 10 to over 100 times compared to LSEC. Like LSEC, aLSEC maintains a computational cost that grows linearly with the number of actors in a network, making it scalable to large sparse networks. We developed the R package aLSEC which implements the proposed methodology.
Details
- Title: Subtitle
- Automated detection of edge clusters via an overfitted mixture prior
- Creators
- Hanh T. D. PhamDaniel K. Sewell - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Network science (Cambridge University Press), Vol.12(1), pp.88-106
- DOI
- 10.1017/nws.2023.22
- ISSN
- 2050-1242
- eISSN
- 2050-1250
- Publisher
- Cambridge University Press
- Language
- English
- Electronic publication date
- 01/19/2024
- Date published
- 03/2024
- Academic Unit
- Biostatistics; Public Policy Center (Archive)
- Record Identifier
- 9984548664402771
Metrics
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