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OverviewFull Product DetailsAuthor: Konstantin Avrachenkov (INRIA Sophia-Antipolis, France) , Maximilien Dreveton (INRIA Sophia-Antipolis, France)Publisher: now publishers Inc Imprint: now publishers Inc ISBN: 9781638280507ISBN 10: 1638280509 Pages: 250 Publication Date: 30 September 2022 Audience: Professional and scholarly , Professional & Vocational Format: Hardback Publisher's Status: Active Availability: In Print  This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us. Language: English Table of Contents1. Introduction 2. Random Graph Models 3. Network Centrality Indices 4. Community Detection in Networks 5. Graph-based Semi-Supervised Learning 6. Community Detection in Temporal Networks 7. Sampling in Networks 8. AppendicesReviewsThis 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 -- Eric D. Kolaczyk The book proposed is a worthwhile one. Network analysis is an active area with a huge amount of work being produced in recent years. The subject of network analysis spans mathematics, probability, statistics, physics and computer science, amongst others. The book focusses on the topics of community detection, dynamic graphs and sampling on graphs. These are all topics of interest to researchers in network analysis and people who analyse network data. Community detection is hugely relevant in applications of network analysis. The book would be useful in providing a formal treatment of many topics of interest to people who use network analysis. The book also focusses on centrality measures which are important, are a full chapter in the book, but downplayed in the description; they should also be emphasised. Brendan Murphy, University College Dublin, Ireland -- Brendan Murphy Author InformationKonstantin Avrachenkov received the master's degree in control theory from St. Petersburg State Polytechnic University in 1996, the Ph.D. degree in mathematics from the University of South Australia in 2000, and the Habilitation (Doctor of Science) degree from the University of Nice Sophia Antipolis in 2010.,Currently, he is the Director of Research at Inria Sophia Antipolis, France. His main research interests are Markov processes, singular perturbation theory, optimization, game theory, and analysis of complex networks. He is an Associate Editor of the International Journal of Performance Evaluation and ACM TOMPECS Maximilien Dreveton is a researcher at Inria Sophia Antipolis, in the team NEO (Network Engineering and Operations). Research interests involve complex networks, especially graph clustering and semi-supervised machine learning. Tab Content 6Author Website:Countries AvailableAll regions |
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