Fr. 170.00

Inferential Network Analysis

English · Hardback

Shipping usually within 3 to 5 weeks

Description

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Pioneering introduction of unprecedented breadth and scope to inferential and statistical methods for network analysis.

List 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.

About the author

Skyler J. Cranmer is the Carter Phillips and Sue Henry Professor of Political Science at The Ohio State University.Bruce A. Desmarais is the DeGrandis-McCourtney Early Career Professor in Political Science at Penn State University.Jason William Morgan is the Vice President for Behavioural Intelligence: Aware, and visiting scholar in Political Science at The Ohio State University.

Summary

Introduces foundational statistical models for network data, augmenting theoretical discussion with applications across the social sciences implemented in the R language. An introductory text or reference for researchers, graduate students, and advanced undergraduate students across the social, mathematical, computational and physical sciences.

Additional text

'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

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