Crane H. Probabilistic Foundations of Statistical Network Analysis

Boca Raton, USA: CRC Press, Taylor & Francis Group, 2018. — 257 p. — (Monographs on Statistics and Applied Probability 157). — ISBN: 1138630152.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-valued Levy process and rewiring models for dynamic networks.Orientation
Binary relational data
Network sampling
Generative models
Statistical modeling paradigm
Vertex exchangeable models
Getting beyond graphons
Relatively exchangeable models
Edge exchangeable models
Relationally exchangeable models
Dynamic network models