scholarly journals Birds of a Feather, Or Friend of a Friend?: Using Exponential Random Graph Models to Investigate Adolescent Social Networks

Demography ◽  
2009 ◽  
Vol 46 (1) ◽  
pp. 103-125 ◽  
Author(s):  
Steven M. Goodreau ◽  
James A. Kitts ◽  
Martina Morris
Keyword(s):  
Social Networks ◽  
Random Graph ◽  
Exponential Random Graph Models ◽  
Graph Models ◽  
Random Graph Models ◽  
Exponential Random Graph

Measurement Error and Attenuation Bias in Exponential Random Graph Models

2016 ◽  
Vol 7 (1-2) ◽  
pp. 29-54
Author(s):  
Yeaji Kim ◽  
Leonardo Antenangeli ◽  
Justin Kirkland
Keyword(s):  
Social Networks ◽  
Random Graph ◽  
Measurement Errors ◽  
Asymptotic Properties ◽  
Exponential Random Graph Models ◽  
Graph Models ◽  
Random Graph Models ◽  
Exponential Random Graph ◽  
The Face ◽  
Distributed Measurement

AbstractExponential Random Graph Models (ERGMs) are becoming increasingly popular tools for estimating the properties of social networks across the social sciences. While the asymptotic properties of ERGMs are well understood, much less is known about how ERGMs perform in the face of violations of the assumptions that drive those asymptotic properties. Given that empirical social networks rarely meet the strenuous assumptions of the ERGM perfectly, practical researchers are often in the position of knowing their coefficients are imperfect, but not knowing precisely how wrong those coefficients may be. In this research, we examine one violation of the asymptotic assumptions of ERGMs – perfectly measured social networks. Using several Monte Carlo simulations, we demonstrate that even randomly distributed measurement errors in networks under study can cause considerable attenuation in coefficients from ERGMs, and do real harm to subsequent hypothesis tests.

Advances in Exponential Random Graph Models

2020 ◽  
pp. 233-253
Author(s):  
Dean Lusher ◽  
Peng Wang ◽  
Julia Brennecke ◽  
Julien Brailly ◽  
Malick Faye ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Influence ◽  
Random Graph ◽  
Statistical Models ◽  
Directed Network ◽  
Exponential Random Graph Models ◽  
Graph Models ◽  
Random Graph Models ◽  
Exponential Random Graph ◽  
Recent Developments

This chapter presents recent developments in exponential random graph models (ERGMs), statistical models for social network structure. ERGMs assume that social networks are composed of various network substructures (or network configurations) like reciprocity, brokerage, or transitive closure, which, combined together, explain how the network came into being. The chapter also discusses recent developments for related models—auto-logistic actor attributes models (ALAAMs)—that examine social influence effects. The chapter focuses on three new types of models that have developed in the past few years: directed network models for social influence, multilevel extensions of ERGMs, and multilevel extensions of ALAAMs. The chapter concludes with three empirical applications to demonstrate what new possibilities exist in the application of these new statistical models for social networks to social science questions.

Sign in / Sign up

Export Citation Format

Share Document