Open Access
Translator Disclaimer
February 2017 Oracle inequalities for network models and sparse graphon estimation
Olga Klopp, Alexandre B. Tsybakov, Nicolas Verzelen
Ann. Statist. 45(1): 316-354 (February 2017). DOI: 10.1214/16-AOS1454

Abstract

Inhomogeneous random graph models encompass many network models such as stochastic block models and latent position models. We consider the problem of statistical estimation of the matrix of connection probabilities based on the observations of the adjacency matrix of the network. Taking the stochastic block model as an approximation, we construct estimators of network connection probabilities—the ordinary block constant least squares estimator, and its restricted version. We show that they satisfy oracle inequalities with respect to the block constant oracle. As a consequence, we derive optimal rates of estimation of the probability matrix. Our results cover the important setting of sparse networks. Another consequence consists in establishing upper bounds on the minimax risks for graphon estimation in the $L_{2}$ norm when the probability matrix is sampled according to a graphon model. These bounds include an additional term accounting for the “agnostic” error induced by the variability of the latent unobserved variables of the graphon model. In this setting, the optimal rates are influenced not only by the bias and variance components as in usual nonparametric problems but also include the third component, which is the agnostic error. The results shed light on the differences between estimation under the empirical loss (the probability matrix estimation) and under the integrated loss (the graphon estimation).

Citation

Download Citation

Olga Klopp. Alexandre B. Tsybakov. Nicolas Verzelen. "Oracle inequalities for network models and sparse graphon estimation." Ann. Statist. 45 (1) 316 - 354, February 2017. https://doi.org/10.1214/16-AOS1454

Information

Received: 1 July 2015; Revised: 1 February 2016; Published: February 2017
First available in Project Euclid: 21 February 2017

zbMATH: 1367.62090
MathSciNet: MR3611494
Digital Object Identifier: 10.1214/16-AOS1454

Subjects:
Primary: 62G05
Secondary: 60C05

Keywords: inhomogeneous random graph , networks , Oracle inequality , sparse graphon , Sparsity , Stochastic block model

Rights: Copyright © 2017 Institute of Mathematical Statistics

JOURNAL ARTICLE
39 PAGES


SHARE
Vol.45 • No. 1 • February 2017
Back to Top