A. D. Barbour
University of Zurich
Cambridge Series in Statistical and Probabilistic Mathematics
Networks: Probability and Statistics
A comprehensive introduction to the probabilistic and statistical analysis of network data, bringing together examples, models, inference, and applications.
This pre-publication version is free to view and download for personal use only. Not for redistribution, re-sale or use in derivative works. © Andrew Barbour and Gesine Reinert 2026.
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NETW2026
30% off through 30 June 2027
About the book
A rigorous guide to network data, models, and inference
Network representations now appear across the sciences: protein interactions, social relationships, communication systems, trade, the web, and many other complex data sources. This book develops the probability and statistics needed to describe such structures, understand uncertainty, and assess competing models.
The treatment begins with concrete data sets and descriptive summaries, then moves through random graph models, branching-process ideas, preferential attachment, graph limits, sampling, estimation, model checking, community detection, comparison, and further topics.
June 2026
Publication date
c. 964 pp.
Hardback volume
978-1-009-65172-1
ISBN
NETW2026
30% discount code
Review
An unusual blend of practical examples, probabilistic treatment of important random graph models, description and analysis of statistical methods, all written with clarity, insight, and competence.
Steffen Lauritzen, Emeritus Professor of Statistics, University of Oxford and University of Copenhagen
Authors
Gesine Reinert
University of Oxford