Inferential Network Analysis
Published on 27. January 2021
978-1-009-02868-4 (ISBN)
Description
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.
Reviews / Votes
'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 HillMore details
Series
Language
English
Place of publication
Cambridge
United Kingdom
Target group
College/higher education
Professional and scholarly
Illustrations
Worked examples or Exercises
File size
10,40 MB
ISBN-13
978-1-009-02868-4 (9781009028684)
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Schweitzer Classification
Other editions
Additional editions
Skyler J. Cranmer | Bruce A. Desmarais | Jason W. Morgan
Inferential Network Analysis
Book
11/2020
Cambridge University Press
€166.50
Shipment within 15-20 days
Persons
Content
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.
