Probabilistic Foundations of Statistical Network Analysis

The book Probabilistic Foundations of Statistical Network Analysis provides a comprehensive and insightful perspective on modern network analysis, covering key concepts such as exchangeable and dynamic network models, network sampling, and network statistics. With a clear and intuitive exposition, it is accessible to a wide range of readers, including professional data scientists, statisticians, and computer scientists, as well as practitioners and researchers in substantive fields. It offers a mathematically rigorous yet non-technical approach, with conceptual insights and more than 50 open research problems and exercises.

\n Format: Paperback / softback
\n Length: 236 pages
\n Publication date: 11 April 2018
\n Publisher: Taylor & Francis Ltd
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Probabilistic Foundations of Statistical Network Analysis presents a comprehensive and insightful perspective on the fundamental principles and significant challenges of modern network analysis. Through its lucid exposition, the book provides essential background for understanding the core concepts behind exchangeable and dynamic network models, network sampling, and network statistics such as sparsity and power law, which are crucial in contemporary data science and machine learning applications. The authors offer a clear and intuitive understanding of the intricate interplay between statistical inference, empirical properties of network data, and technical concepts from probability theory. This mathematically rigorous yet non-technical presentation makes the book accessible to professional data scientists, statisticians, computer scientists, practitioners, and researchers across various substantive fields. Newcomers and non-quantitative researchers will find the book's conceptual approach invaluable for developing a deeper intuition about technical ideas from statistics and probability, while experts and graduate students will appreciate its comprehensive coverage of new topics, including edge exchangeability, relative exchangeability, graphon and graphex models, and graph-valued Levy process and rewiring models for dynamic networks.

In addition to its core content, the book includes incisive commentary that challenges the reader to explore the boundaries of this emerging discipline. With an approachable exposition and over 50 open research problems and exercises with solutions, this book is an ideal resource for advanced undergraduate and graduate students interested in modern network analysis, data science, machine learning, and statistics. By delving into the probabilistic foundations of network analysis, Probabilistic Foundations of Statistical Network Analysis offers a valuable contribution to the field and empowers readers to leverage the power of networks for solving complex problems and advancing scientific understanding.

\n Weight: 394g\n
Dimension: 155 x 234 x 21 (mm)\n
ISBN-13: 9781138630154\n \n