Machine generated contents note: pt. I Dependence And Interdependence -- 1.Promises and Pitfalls of Inferential Network Analysis -- 1.1.A Basis for Considering Networks -- 1.2.Networks and Complex Statistical Dependence -- 1.3.Methods Covered in This Book -- 2.Detecting and Adjusting for Network Dependencies -- 2.1.Detecting Dependencies: Conditional Uniform Graph Tests -- 2.2.The Quadratic Assignment Procedure (QAP) -- 2.3.Wrapping Up -- 2.4.Self-Study Problems -- pt. II The Family Of Exponential Random Graph Models (Ergms) -- 3.The Basic ERGM -- 3.1.Introduction -- 3.2.The Exponential Random Graph Model (ERGM) -- 3.3.ERGM Specification: A Brief Introduction -- 3.4.Model Fit -- 3.5.Interpretation -- 3.6.Limitations -- 3.7.Wrapping Up -- 3.8.Self-Study Problems -- 4.ERGM Specification -- 4.1.Starting with Theory -- 4.2.Exogenous Covariate Effects -- 4.3.Endogenous Network Effects -- 4.4.Creating New Statistics -- 4.5.Bipartite ERGMs -- 4.6.Wrapping Up -- 4.7.Self-Study Problems -- 5.Estimation and Degeneracy -- 5.1.Methods for Estimating ERGM -- 5.2.Problem of Degeneracy -- 5.3.Adjusting Specifications to Correct Degeneracy and Improve Model Fit -- 5.4.Other Estimation Methods for ERGMs -- 5.5.Wrapping Up -- 5.6.Self-Study Problems -- 6.ERG Type Models for Longitudinally Observed Networks -- 6.1.Introduction -- 6.2.Data Considerations -- 6.3.The Temporal Exponential Random Graph Model (TERGM) -- 6.4.TERGM Specification -- 6.5.To Pool or Not to Pool? Temporal Stability of Effects -- 6.6.Estimation -- 6.7.The Stochastic Actor-Oriented Model (SAOM) -- 6.8.Wrapping Up -- 6.9.Self-Study Problems -- 7.Valued-Edge ERGMs: The Generalized ERGM (GERGM) -- 7.1.GERGM Definition -- 7.2.Specifying Processes on Weighted Networks -- 7.3.Avoiding Degeneracy in the GERGM -- 7.4.Parameter Estimation -- 7.5.Applications in the Literature -- 7.6.Wrapping Up -- 7.7.Self-Study Problems -- pt. III LATENT SPACE NETWORK MODELS -- 8.The Basic Latent Space Model -- 8.1.Introduction -- 8.2.Motivation: Theoretical and Mathematical Perspective -- 8.3.The Euclidean Latent Space Model -- 8.4.Model Convergence -- 8.5.Model Fit -- 8.6.Model Specification -- 8.7.Interpretation of Latent Space Models -- 8.8.Strengths, Assumptions, and Limitations of the Latent Space Model -- 8.9.Wrapping Up -- 8.10.Self-Study Problems -- 9.Identification, Estimation, and Interpretation of the Latent Space Model -- 9.1.Parameter Identification -- 9.2.Identification: Some Solutions -- 9.3.Interpreting the Latent Space -- 9.4.The Problem with Isolates -- 9.5.Estimation -- 9.6.Wrapping Up -- 10.Extending the Latent Space Model -- 10.1.Introduction -- 10.2.Valued-Edge Networks -- 10.3.Cluster Models -- 10.4.Random Effects Models -- 10.5.The Additive and Multiplicative Effects Latent Factor Model (LFM) -- 10.6.Other Extensions -- 10.7.Wrapping Up -- 10.8.Self-Study Problems.
Summary:
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.
This resource is supported by the Institute of Museum and Library Services under the provisions of the Library Services and Technology Act as administered by State Library of Iowa.