3 citations · 3 across the 3 of their papers we have counts for
5 papers
Learning cross-layer dependence structure in multilayer networks
Jiaheng Li, Jonathan R. Stewart
We propose a novel class of separable multilayer network models to capture cross-layer dependencies in multilayer networks, enabling the analysis of how interactions in one or more…
Model selection for network data based on spectral information
Jairo Ivan Peña Hidalgo, Jonathan R. Stewart
We introduce a new methodology for model selection in the context of modeling network data. The statistical network analysis literature has developed many different classes of netw…
Pseudo-likelihood-based -estimation of random graphs with dependent edges and parameter vectors of increasing dimension
Jonathan R. Stewart, Michael Schweinberger
An important question in statistical network analysis is how to estimate models of discrete and dependent network data with intractable likelihood functions, without sacrificing co…
Exponential-Family Models of Random Graphs: Inference in Finite-, Super-, and Infinite Population Scenarios
Michael Schweinberger, Pavel N. Krivitsky, Carter T. Butts +1
Exponential-family Random Graph Models (ERGMs) constitute a large statistical framework for modeling sparse and dense random graphs, short- and long-tailed degree distributions, co…
Concentration and consistency results for canonical and curved exponential-family models of random graphs
Michael Schweinberger, Jonathan Stewart
Statistical inference for exponential-family models of random graphs with dependent edges is challenging. We stress the importance of additional structure and show that additional…