Inferential Network Analysis
Inferential Network Analysis
Bruce A. Desmarais, Pennsylvania State University
Jason W. Morgan, The Ohio State University
This unique textbook provides an introduction to statistical inference with network data. The authors present a self-contained derivation and mathematical formulation of methods, review examples, and real-world applications, as well as provide data and code in the R environment that can be customised. Inferential network analysis transcends fields, and examples from across the social sciences are discussed (from management to electoral politics), which can be adapted and applied to a panorama of research. From scholars to undergraduates, spanning the social, mathematical, computational and physical sciences, readers will be introduced to inferential network models and their extensions. The exponential random graph model and latent space network model are paid particular attention and, fundamentally, the reader is given the tools to independently conduct their own analyses.
- For the first time provides wide-ranging and descriptive treatment of inferential network analysis that transcends fields
- Applies detailed theoretical discussions in non-specialist language
- Implements real-world and customisable examples in each chapter, complete with data and R code
Reviews & endorsements
‘The family of exponential random graph models have advanced with a number of extensions in recent years, many of them developed by the present authors. Encapsulating these advances with other methods of inferential analysis in a single reference that combines essential theory with hands-on examples makes this book a must-have for network modeling practitioners who want to use these powerful tools.' Peter Mucha, UNC Chapel Hill
Product details
January 2021Adobe eBook Reader
9781009028684
0 pages
This ISBN is for an eBook version which is distributed on our behalf by a third party.
Table of Contents
- Part I. Dependence and Interdependence:
- 1. Promises and Pitfalls of Inferential Network Analysis
- 2. Detecting and Adjusting for Network Dependencies
- Part II. The Family of Exponential Random Graph Models (ERGMs):
- 3. The Basic ERGM
- 4. ERGM Specification
- 5. Estimation and Degeneracy
- 6. ERG Type Models for Longitudinally Observed Networks
- 7. Valued-Edge ERGMs: The Generalized ERGM (GERGM)
- Part III. Latent Space Network Models:
- 8. The Basic Latent Space Model
- 9. Identification, Estimation and Interpretation of the Latent Space Model
- 10. Extending the Latent Space Model.
