Statistical Analysis of Networks

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 Supérieure 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.

• Chapter 1. IntroductionChapter 2. Random Graph ModelsChapter 3. Network Centrality IndicesChapter 4. Community Detection in NetworksChapter 5. Graph-based Semi-Supervised LearningChapter 6. Community Detection in Temporal NetworksChapter 7. Sampling in NetworksAppendices

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.