174 citations · 415 across the 18 of their papers we have counts for
6 papers · 1 filter
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…
A note on the role of projectivity in likelihood-based inference for random graph models
Michael Schweinberger, Pavel N. Krivitsky, Carter T. Butts
There is widespread confusion about the role of projectivity in likelihood-based inference for random graph models. The confusion is rooted in claims that projectivity, a form of m…
A two-stage working model strategy for network analysis under Hierarchical Exponential Random Graph Models
Ming Cao
Social networks as a representation of relational data, often possess multiple types of dependency structures at the same time. There could be clustering (beyond homophily) at a ma…
Large-scale estimation of random graph models with local dependence
Sergii Babkin, Jonathan Stewart, Xiaochen Long +1
A class of random graph models is considered, combining features of exponential-family models and latent structure models, with the goal of retaining the strengths of both of them…
Consistent structure estimation of exponential-family random graph models with block structure
Michael Schweinberger
We consider the challenging problem of statistical inference for exponential-family random graph models based on a single observation of a random graph with complex dependence. To…
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…