CO-CLUSTERING SEPARATELY EXCHANGEABLE NETWORK DATA

• 成果类型: Article
• 署名作者: Choi, David; Wolfe, Patrick J.
• 署名单位: Carnegie Mellon University; University of London; University College London
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/13-AOS1173
• 发表日期: 2014
• 页码: 29-63
• 关键词: stochastic blockmodels; convergent sequences; community detection; models

摘要

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 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.