The Annals of Statistics

Co-clustering separately exchangeable network data

David Choi and Patrick J. Wolfe

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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.

Article information

Ann. Statist., Volume 42, Number 1 (2014), 29-63.

First available in Project Euclid: 15 January 2014

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 62G05: Estimation
Secondary: 05C80: Random graphs [See also 60B20] 60B20: Random matrices (probabilistic aspects; for algebraic aspects see 15B52)

Bipartite graph network clustering oracle inequality profile likelihood statistical network analysis stochastic blockmodel and co-blockmodel


Choi, David; Wolfe, Patrick J. Co-clustering separately exchangeable network data. Ann. Statist. 42 (2014), no. 1, 29--63. doi:10.1214/13-AOS1173.

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