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- Autorius: A. D. Barbour, Gesine Reinert
- Leidėjas: Cambridge University Press
- Metai: 2026
- Puslapiai: 964
- ISBN-10: 1009651722
- ISBN-13: 9781009651721
- Formatas: x x cm, kieti viršeliai
- Kalba: Anglų
- Kaina galioja įvedus kodą: ENG
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Aprašymas
From social networks to biological systems, networks are a fundamental part of modern life. Network analysis is increasingly popular across the mathematical, physical, life and social sciences, offering insights into a range of phenomena, from developing new drugs based on intracellular interactions, to understanding the influence of social interactions on behaviour patterns. This book provides a toolkit for analyzing random networks, together with theoretical justification of the methods proposed. It combines methods from both probability and statistics, teaching how to build and analyze plausible models for random networks, and how to validate such models, to detect unusual features in the data, and to make predictions. Theoretical results are motivated by applications across a range of fields, and classical data sets are used for illustration throughout the book. This book offers a comprehensive introduction to the field for graduate students and researchers.
Kaina galioja įvedus kodą: ENG
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- Autorius: A. D. Barbour, Gesine Reinert
- Leidėjas: Cambridge University Press
- Metai: 2026
- Puslapiai: 964
- ISBN-10: 1009651722
- ISBN-13: 9781009651721
- Formatas: x x cm, kieti viršeliai
- Kalba: Anglų
From social networks to biological systems, networks are a fundamental part of modern life. Network analysis is increasingly popular across the mathematical, physical, life and social sciences, offering insights into a range of phenomena, from developing new drugs based on intracellular interactions, to understanding the influence of social interactions on behaviour patterns. This book provides a toolkit for analyzing random networks, together with theoretical justification of the methods proposed. It combines methods from both probability and statistics, teaching how to build and analyze plausible models for random networks, and how to validate such models, to detect unusual features in the data, and to make predictions. Theoretical results are motivated by applications across a range of fields, and classical data sets are used for illustration throughout the book. This book offers a comprehensive introduction to the field for graduate students and researchers.
Atsiliepimai