Inferential network analysis/ Skyler J. Cranmer, Bruce A. Desmarais and Jason W. Morgan

Includes bibliographical references and index

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 -- ERGM specification -- 5. Estimation and degeneracy -- 6. ERG type models for longitudinally observed networks -- 7. Valued-Edge ERGMs: the generalised 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

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.