Skyler J. Cranmer, Bruce A. Desmarais, Jason W. Morgan

Inferential Network Analysis

• Broschiertes Buch

Produktbeschreibung

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.

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Produktdetails

• Analytical Methods for Social Research
• Verlag: Cambridge University Press
• Seitenzahl: 314
• Erscheinungstermin: 19. November 2020
• Englisch
• Abmessung: 222mm x 149mm x 20mm
• Gewicht: 470g
• ISBN-13: 9781316610855
• ISBN-10: 1316610853
• Artikelnr.: 60064297

Autorenporträt

Skyler J. Cranmer is the Carter Phillips and Sue Henry Professor of Political Science at The Ohio State University.

Inhaltsangabe

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.

Rezensionen

'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