Statistical Analysis of Networks
Published on 6. October 2022
Book
Hardback
248 pages
978-1-63828-050-7 (ISBN)
Description
The ebook edition of this title is Open Access and freely available to read online.
This book is a general introduction to the statistical analysis of networks, and can serve both as a research monograph and as a textbook. Numerous fundamental tools and concepts needed for the analysis of networks are presented, such as network modeling, community detection, graph-based semi-supervised learning and sampling in networks. The description of these concepts is self-contained, with both theoretical justifications and applications provided for the presented algorithms. Researchers, including postgraduate students, working in the area of network science, complex network analysis, or social network analysis, will find up-to-date statistical methods relevant to their research tasks. This book can also serve as textbook material for courses related to the statistical approach to the analysis of complex networks.
In general, the chapters are fairly independent and self-supporting, and the book could be used for course composition "a la carte". Nevertheless, Chapter 2 is needed to a certain degree for all parts of the book. It is also recommended to read Chapter 4 before reading Chapters 5 and 6, but this is not absolutely necessary. Reading Chapter 3 can also be helpful before reading Chapters 5 and 7. As prerequisites for reading this book, a basic knowledge in probability, linear algebra and elementary notions of graph theory is advised. Appendices describing required notions from the above mentioned disciplines have been added to help readers gain further understanding.
This book is a general introduction to the statistical analysis of networks, and can serve both as a research monograph and as a textbook. Numerous fundamental tools and concepts needed for the analysis of networks are presented, such as network modeling, community detection, graph-based semi-supervised learning and sampling in networks. The description of these concepts is self-contained, with both theoretical justifications and applications provided for the presented algorithms. Researchers, including postgraduate students, working in the area of network science, complex network analysis, or social network analysis, will find up-to-date statistical methods relevant to their research tasks. This book can also serve as textbook material for courses related to the statistical approach to the analysis of complex networks.
In general, the chapters are fairly independent and self-supporting, and the book could be used for course composition "a la carte". Nevertheless, Chapter 2 is needed to a certain degree for all parts of the book. It is also recommended to read Chapter 4 before reading Chapters 5 and 6, but this is not absolutely necessary. Reading Chapter 3 can also be helpful before reading Chapters 5 and 7. As prerequisites for reading this book, a basic knowledge in probability, linear algebra and elementary notions of graph theory is advised. Appendices describing required notions from the above mentioned disciplines have been added to help readers gain further understanding.
Reviews / Votes
This is an interesting book. Models are introduced in the first chapter, and then centralities in the second. Community detection is certainly a popular topic, especially among those working in complex network analysis. And the author is certainly correct that community detection in dynamic networks has received comparatively less exposure. There are models for dynamic networks and extensions of characterizations (like centrality and otherwise) for dynamic networks. Finally, the chapter on sampling is of interest, and not something that is usually covered in network texts. In my opinion the book should have good market appeal. -- Eric D. Kolaczyk, Boston University, USA.More details
Language
English
Place of publication
United States
Publishing group
Emerald Publishing Inc
Target group
Professional and scholarly
Dimensions
Height: 240 mm
Width: 161 mm
Thickness: 18 mm
Weight
540 gr
ISBN-13
978-1-63828-050-7 (9781638280507)
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Schweitzer Classification
Persons
Konstantin Avrachenkov received his Master degree in Control Theory from St. Petersburg State Polytechnic University (1996), Ph.D. degree in Mathematics from University of South Australia (2000) and Habilitation from University of Nice Sophia Antipolis (2010). Currently, he is a Director of Research at Inria Sophia Antipolis, France. He is an associate editor of the International Journal of Performance Evaluation, Probability in the Engineering and Informational Sciences, ACM TOMPECS, Stochastic Models and IEEE Network Magazine. He has won 5 best paper awards. His main theoretical research interests are Markov chains, Markov decision processes, random graphs and singular perturbations. He applies these methodological tools to the modeling and control of networks, and to design data mining and machine learning algorithms.
Maximilien Dreveton received his Bachelor and Master degrees in the field of Physics from Ecole Normale Superieure de Lyon, France, in 2013 and 2015. He obtained his Ph.D. in Computer Science from Inria Sophia Antipolis in 2022 and is currently a postdoctoral researcher at EPFL in Lausanne, Switzerland. His research interests include statistical analysis of random graphs, and more particularly community detection.
Maximilien Dreveton received his Bachelor and Master degrees in the field of Physics from Ecole Normale Superieure de Lyon, France, in 2013 and 2015. He obtained his Ph.D. in Computer Science from Inria Sophia Antipolis in 2022 and is currently a postdoctoral researcher at EPFL in Lausanne, Switzerland. His research interests include statistical analysis of random graphs, and more particularly community detection.
Content
Chapter 1. Introduction
Chapter 2. Random Graph Models
Chapter 3. Network Centrality Indices
Chapter 4. Community Detection in Networks
Chapter 5. Graph-based Semi-Supervised Learning
Chapter 6. Community Detection in Temporal Networks
Chapter 7. Sampling in Networks
Appendices
Chapter 2. Random Graph Models
Chapter 3. Network Centrality Indices
Chapter 4. Community Detection in Networks
Chapter 5. Graph-based Semi-Supervised Learning
Chapter 6. Community Detection in Temporal Networks
Chapter 7. Sampling in Networks
Appendices