β Scribed by Skyler J. Cranmer, Bruce A. Desmarais, Jason W. Morgan
No coin nor oath required. For personal study only.
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.
List of Figures
List of Tables
Notation and Acronyms
Preface
Acknowledgments
Part IDependence and Interdependence
1Promises and Pitfalls of Inferential Network Analysis
1.1A Basis for Considering Networks
1.2Networks and Complex Statistical Dependence
1.3Methods Covered in This Book
2Detecting and Adjusting for Network Dependencies
2.1Detecting Dependencies: Conditional Uniform Graph Tests
2.2The Quadratic Assignment Procedure (QAP)
2.3Wrapping Up
2.4Self-Study Problems
Part IIThe Family of Exponential Random Graph Models (ERGMs)
3The Basic ERGM
3.1Introduction
3.2The Exponential Random Graph Model (ERGM)
3.3ERGM Specification: A Brief Introduction
3.4Model Fit
3.5Interpretation
3.6Limitations
3.7Wrapping Up
3.8Self-Study Problems
4ERGM Specification
4.1Starting with Theory
4.2Exogenous Covariate Effects
4.3Endogenous Network Effects
4.4Creating New Statistics
4.5Bipartite ERGMs
4.6Wrapping Up
4.7Self-Study Problems
5Estimation and Degeneracy
5.1Methods for Estimating ERGM
5.2Problem of Degeneracy
5.3Adjusting Specifications to Correct Degeneracy and Improve Model Fit
5.4Other Estimation Methods for ERGMs
5.5Wrapping Up
5.6Self-Study Problems
6ERG Type Models for Longitudinally Observed Networks
6.1Introduction
6.2Data Considerations
6.3The Temporal Exponential Random Graph Model (TERGM)
6.4TERGM Specification
6.5To Pool or Not to Pool? Temporal Stability of Effects
6.6Estimation
6.7The Stochastic ActorβOriented Model (SAOM)
6.8Wrapping Up
6.9Self-Study Problems
π SIMILAR VOLUMES
I bought this book for use in a class on rhetoric. It is difficult reading, but if you stick with it, it can help to give you a new perspcetive on thinking about the discourse around you.
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