Open Access
February 2014 Co-clustering separately exchangeable network data
David Choi, Patrick J. Wolfe
Ann. Statist. 42(1): 29-63 (February 2014). DOI: 10.1214/13-AOS1173


This article establishes the performance of stochastic blockmodels in addressing the co-clustering problem of partitioning a binary array into subsets, assuming only that the data are generated by a nonparametric process satisfying the condition of separate exchangeability. We provide oracle inequalities with rate of convergence $\mathcal{O}_{P}(n^{-1/4})$ corresponding to profile likelihood maximization and mean-square error minimization, and show that the blockmodel can be interpreted in this setting as an optimal piecewise-constant approximation to the generative nonparametric model. We also show for large sample sizes that the detection of co-clusters in such data indicates with high probability the existence of co-clusters of equal size and asymptotically equivalent connectivity in the underlying generative process.


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David Choi. Patrick J. Wolfe. "Co-clustering separately exchangeable network data." Ann. Statist. 42 (1) 29 - 63, February 2014.


Published: February 2014
First available in Project Euclid: 15 January 2014

zbMATH: 1294.62059
MathSciNet: MR3161460
Digital Object Identifier: 10.1214/13-AOS1173

Primary: 62G05
Secondary: 05C80 , 60B20

Keywords: Bipartite graph , network clustering , Oracle inequality , profile likelihood , statistical network analysis , stochastic blockmodel and co-blockmodel

Rights: Copyright © 2014 Institute of Mathematical Statistics

Vol.42 • No. 1 • February 2014
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