Concentration and consistency results for canonical and curved exponential-family models of random graphs

DOI10.1214/19-AOS1810zbMath1439.05206arXiv1702.01812OpenAlexW3007771191WikidataQ115517744 ScholiaQ115517744MaRDI QIDQ2176626

Michael Schweinberger, Jonathan R. Stewart

Publication date: 5 May 2020

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1702.01812

zbMATH Keywords

exponential familiessocial networkscurved exponential families\(M\)-estimatorsexponential-family random graph modelsmultilevel networks

Mathematics Subject Classification ID

Social networks; opinion dynamics (91D30) Point estimation (62F10) Random graphs (graph-theoretic aspects) (05C80) Sufficient statistics and fields (62B05)

Related Items (9)

Large-scale estimation of random graph models with local dependence ⋮ Discussion to: \textit{Bayesian graphical models for modern biological applications} by Y. Ni, V. Baladandayuthapani, M. Vannucci and F.C. Stingo ⋮ A dynamic process reference model for sparse networks with reciprocity ⋮ Concentration and consistency results for canonical and curved exponential-family models of random graphs ⋮ Statistical inference for continuous‐time Markov processes with block structure based on discrete‐time network data ⋮ Discussion of “A Tale of Two Datasets: Representativeness and Generalisability of Inference for Samples of Networks” by Pavel N. Krivitsky, Pietro Coletti, and Niel Hens ⋮ Exponential-family models of random graphs: inference in finite, super and infinite population scenarios ⋮ Consistent structure estimation of exponential-family random graph models with block structure ⋮ Statistics of the two star ERGM

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Exponential-family random graph models for valued networks
• On the question of effective sample size in network modeling: an asymptotic inquiry
• Consistency under sampling of exponential random graph models
• Maximum likelihood estimation in the \(\beta\)-model
• Adjusting for network size and composition effects in exponential-family random graph models
• Random graphs with a given degree sequence
• Mixing time of exponential random graphs
• Logit models and logistic regressions for social networks. I: An introduction to Markov graphs and \(p^*\)
• High-dimensional Ising model selection using \(\ell _{1}\)-regularized logistic regression
• Modeling social networks from sampled data
• Statistical analysis of network data. Methods and models
• The geometry of exponential families
• Defining the curvature of a statistical problem (with applications to second order efficiency)
• Concentration of measure inequalities for Markov chains and \(\Phi\)-mixing processes.
• Likelihood inference in exponential families and directions of recession
• On the geometry of discrete exponential families with application to exponential random graph models
• A statistical framework for modern network science
• Concentration and consistency results for canonical and curved exponential-family models of random graphs
• Inference in Ising models
• Estimating and understanding exponential random graph models
• Estimation in spin glasses: a first step
• Concentration inequalities for dependent random variables via the martingale method
• On Graphical Models via Univariate Exponential Family Distributions
• Instability, Sensitivity, and Degeneracy of Discrete Exponential Families
• A Flexible Parameterization for Baseline Mean Degree in Multiple-Network ERGMs
• Goodness of Fit of Social Network Models
• Markov Graphs
• Inference and missing data
• Divide and conquer martingales and the number of triangles in a random graph
• Estimation and Prediction for Stochastic Blockstructures
• Concentration of non‐Lipschitz functions and applications
• The infamous upper tail
• The random triangle model
• Random Networks, Graphical Models and Exchangeability
• Statistical Inference in a Directed Network Model With Covariates
• Local Dependence in Random Graph Models: Characterization, Properties and Statistical Inference
• Asymptotics in directed exponential random graph models with an increasing bi-degree sequence

This page was built for publication: Concentration and consistency results for canonical and curved exponential-family models of random graphs