The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Uncovering latent structure in valued graphs: A variational approach
Open accessAbstract
As more and more network-structured data sets are available, the statistical analysis of valued graphs has become common place. Looking for a latent structure is one of the many strategies used to better understand the behavior of a network. Several methods already exist for the binary case. We present a model-based strategy to uncover groups of nodes in valued graphs. This framework can be used for a wide span of parametric random graphs models and allows to include covariates. Variational tools allow us to achieve approximate maximum likelihood estimation of the parameters of these models. We provide a simulation study showing that our estimation method performs well over a broad range of situations. We apply this method to analyze host–parasite interaction networks in forest ecosystems.
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Cites
McLachlan, G., & Peel, D. (2000) · Wiley Series in Probability and Statistics · doi:10.1002/0471721182 · Cited by 10,991
An Introduction to Variational Methods for Graphical Models
Jordan, M. I., Ghahramani, Z., Jaakkola, T. S., & Saul, L. K. (1999) · Machine Learning · doi:10.1023/a:1007665907178 · Cited by 3,791
(2005) · Edinburgh Research Explorer (University of Edinburgh) · doi:10.5555/1046920.1088695 · Cited by 650
Cited by
Exponential-family random graph models for valued networks
Krivitsky, P. N. (2012) · Electronic Journal of Statistics · doi:10.1214/12-ejs696 · Cited by 295
Pseudo-likelihood methods for community detection in large sparse networks
Amini, A. A., Chen, A., Bickel, P. J., & Levina, E. (2013) · The Annals of Statistics · doi:10.1214/13-aos1138 · Cited by 259
Metadata
- Authors
- Mariadassou, M., Robin, S., & Vacher, C.
- Venue
- The Annals of Applied Statistics
- Publisher
- Institute of Mathematical Statistics
- Published
- 2010-06-01
- Volume
- 4
- Issue
- 2
- Pages
- 715-742
- ISSN
- 1932-6157 (ISSN-L)
- OpenAlex
- W2018312367
- Cited by
- 220
- References
- 3
- FWCI
- 7.39
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Mariadassou, M., Robin, S., & Vacher, C. (2010). Uncovering latent structure in valued graphs: A variational approach. The Annals of Applied Statistics. https://doi.org/10.1214/10-aoas361
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
