Inferential Network Analysis

This book offers a foundational understanding of statistical models for network data, ideal for researchers and students in various scientific fields. Inferential Network Analysis provides practical applications and tools.

The book Inferential Network Analysis serves as a comprehensive guide to foundational statistical models specifically designed for network data. It adeptly combines theoretical discussions with practical applications across various fields within the social sciences, all implemented using the R programming language. This makes it an ideal resource for researchers, graduate students, and advanced undergraduates who are engaged in disciplines such as social, mathematical, computational, and physical sciences.

Within Inferential Network Analysis, the authors offer a self-contained derivation of methods, alongside a thorough mathematical formulation. Readers will find a wealth of examples and real-world applications that illustrate the concepts discussed. The inclusion of data and code in the R environment allows for customization, enabling users to adapt the material to their specific research needs. This unique approach ensures that the text is not only informative but also practical, catering to a diverse audience.

The book emphasizes inferential network analysis, showcasing examples from a wide array of social science fields, including management and electoral politics. By focusing on models like the exponential random graph model and latent space network model, Inferential Network Analysis equips readers with the necessary tools to conduct their own analyses independently. Its pioneering approach offers an unprecedented breadth and scope in the realm of inferential and statistical methods for network analysis, making it an essential addition to any academic library.