Inferential Network Analysis

AvSkyler J. Cranmer,Bruce A. Desmarais

Häftad, Engelska, 2020

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Beskrivning

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.

Produktinformation

Utgivningsdatum:2020-11-19
Mått:151 x 226 x 18 mm
Vikt:470 g
Format:Häftad
Språk:Engelska
Serie:Analytical Methods for Social Research
Antal sidor:316
Förlag:Cambridge University Press
ISBN:9781316610855

Utforska kategorier

Referensverk och tvärvetenskap inom Samhälle och politik

Mer om författaren

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.

Recensioner i media

'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

Innehållsförteckning

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.