.png){kind=icon}
Return
Social Network Modeling
.png){kind=icon}
OA
AI
DOI：10.1146/annurev-statistics-031017-100746.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The development of stochastic models for the analysis of social networks is an important growth area in contemporary statistics. The last few decades have witnessed the rapid development of a variety of statistical models capable of representing the global structure of an observed network in terms of underlying generating mechanisms. The distinctive feature of statistical models for social networks is their ability to represent directly the dependence relations that these mechanisms entail. In this review, we focus on models for single network observations, particularly on the family of exponential random graph models. After defining the models, we discuss issues of model specification, estimation and assessment. We then review model extensions for the analysis of other types of network data, provide an empirical example, and give a selective overview of empirical studies that have adopted the basic model and its many variants. We conclude with an outline of the current analytical challenges.
Keywords:
cross-sectional network data
exponential random graph models
social networks
tie dependence
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
8.7
Papers:
211
Citations:
2.4K
Organization
View more organizations
Cited Papers
ShareSaveShareSaveHow Semantically Labeled Scent-Gender Associations Influence the Evaluations of Scent and Texture
OAAI
OAAIShareSaveShareSaveEffects of post-training administration of LY341495, as an mGluR2/3 antagonist on spatial memory deficit in rats fed with high-fat diet.png)
IBRO ReportsOAAI
IBRO ReportsIF0
OAAIShareSaveShareSave View more