The ground truth about metadata and community detection in networks

L Peel, DB Larremore, A Clauset - Science advances, 2017 - science.org
Across many scientific domains, there is a common need to automatically extract a simplified
view or coarse-graining of how a complex system's components interact. This general task is …

[LIBRO][B] Inferential network analysis

SJ Cranmer, BA Desmarais, JW Morgan - 2020 - books.google.com
This unique textbook provides an introduction to statistical inference with network data. The
authors present a self-contained derivation and mathematical formulation of methods …

Name your friends, but only five? the importance of censoring in peer effects estimates using social network data

A Griffith - Journal of Labor Economics, 2022 - journals.uchicago.edu
Empirical peer effects research often employs censored peer data. Individuals may list only
a fixed number of links, implying mismeasured peer variables. I first document that censoring …

Exponential-Family Models of Random Graphs

M Schweinberger, PN Krivitsky, CT Butts, JR Stewart - Statistical Science, 2020 - JSTOR
Exponential-family Random Graph Models (ERGMs) constitute a large statistical framework
for modeling dense and sparse random graphs with short-or long-tailed degree distributions …

Recent integrations of latent variable network modeling with psychometric models

S Wang - Frontiers in psychology, 2021 - frontiersin.org
The combination of network modeling and psychometric models has opened up exciting
directions of research. However, there has been confusion surrounding differences among …

Bayesian regression with undirected network predictors with an application to brain connectome data

S Guha, A Rodriguez - Journal of the American Statistical …, 2021 - Taylor & Francis
This article focuses on the relationship between a measure of creativity and the human brain
network for subjects in a brain connectome dataset obtained using a diffusion weighted …