The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Pseudo-likelihood methods for community detection in large sparse networks
Open accessAbstract
Many algorithms have been proposed for fitting network models with communities, but most of them do not scale well to large networks, and often fail on sparse networks. Here we propose a new fast pseudo-likelihood method for fitting the stochastic block model for networks, as well as a variant that allows for an arbitrary degree distribution by conditioning on degrees. We show that the algorithms perform well under a range of settings, including on very sparse networks, and illustrate on the example of a network of political blogs. We also propose spectral clustering with perturbations, a method of independent interest, which works well on sparse networks where regular spectral clustering fails, and use it to provide an initial value for pseudo-likelihood. We prove that pseudo-likelihood provides consistent estimates of the communities under a mild condition on the starting value, for the case of a block model with two communities.
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Cites
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The political blogosphere and the 2004 U.S. election: divided they blog
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Cited by
Matrix estimation by Universal Singular Value Thresholding
Chatterjee, S. (2015) · The Annals of Statistics · doi:10.1214/14-aos1272 · Cited by 465
Review on statistical methods for gene network reconstruction using expression data
Wang, Y. X. R., & Huang, H. (2014) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2014.03.040 · Cited by 182
Metadata
- Authors
- Amini, A. A., Chen, A., Bickel, P. J., & Levina, E.
- Venue
- The Annals of Statistics
- Publisher
- Institute of Mathematical Statistics
- Published
- 2013-08-01
- Volume
- 41
- Issue
- 4
- Pages
- 2097-2122
- ISSN
- 0090-5364 (ISSN-L)
- OpenAlex
- W1973207991
- Cited by
- 259
- References
- 30
- FWCI
- 8.13
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Amini, A. A., Chen, A., Bickel, P. J., & Levina, E. (2013). Pseudo-likelihood methods for community detection in large sparse networks. The Annals of Statistics. https://doi.org/10.1214/13-aos1138
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