Probabilistic Foundations of Statistical Network Analysis [Hardcover]

Introductory remarks. Modeling principles. Modeling network structure. Model properties and their signi cance in statistical inference. Framework for modeling.

The book discusses fundamental considerations for modeling network data of various kinds. The book's major novelty is the emphasis on understanding and building models from first principles, rather than choosing from a list of models that are already available. A main goal of the book is to explain the logic and rationale behind the technical aspects of recent work on network modeling and to discuss how those considerations compare to alternative approaches.

Probabilistic Foundations of Statistical Network Analysis presents a fresh and insightful perspective on the fundamental tenets and major challenges of modern network analysis. Its lucid exposition provides necessary background for understanding the essential ideas behind exchangeable and dynamic network models, network sampling, and network statistics such as sparsity and power law, all of which play a central role in contemporary data science and machine learning applications. The book rewards readers with a clear and intuitive understanding of the subtle interplay between basic principles of statistical inference, empirical properties of network data, and technical concepts from probability theory. Its mathematically rigorous, yet non-technical, exposition makes the book accessible to professional data scientists, statisticians, and computer scientists as well as practitioners and researchers in substantive fields. Newcomers and non-quantitative researchers will find its conceptual approach invaluable for developing intuition about technical ideas from statistics and probability, while experts and graduate students will find the book a handy reference for a wide range of new topics, including edge exchangeability, relative exchangeability, graphon and graphex models, and graph-val–