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Published by Cambridge University Press, Cambridge, 2016
ISBN 10: 110717287X ISBN 13: 9781107172876
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Hardcover. Condition: new. Hardcover. This rigorous introduction to network science presents random graphs as models for real-world networks. Such networks have distinctive empirical properties and a wealth of new models have emerged to capture them. Classroom tested for over ten years, this text places recent advances in a unified framework to enable systematic study. Designed for a master's-level course, where students may only have a basic background in probability, the text covers such important preliminaries as convergence of random variables, probabilistic bounds, coupling, martingales, and branching processes. Building on this base - and motivated by many examples of real-world networks, including the Internet, collaboration networks, and the World Wide Web - it focuses on several important models for complex networks and investigates key properties, such as the connectivity of nodes. Numerous exercises allow students to develop intuition and experience in working with the models. Network science is one of the fastest growing areas in science and business. This classroom-tested, self-contained book is designed for master's-level courses and provides a rigorous treatment of random graph models for networks, featuring many examples of real-world networks for motivation and numerous exercises to build intuition and experience. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Published by Cambridge University Press, 2016
ISBN 10: 110717287X ISBN 13: 9781107172876
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Published by Cambridge University Press, 2016
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Add to basketHardcover. Condition: Good. No dustjacket as issued. Visible indent about 1 inch long to bottom left of front cover, otherwise light shelf wear. Binding tight. All pages intact and free of marks.
Published by Cambridge University Press, 2016
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Published by Cambridge University Press, 2016
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Add to basketHardback. Condition: New. This rigorous introduction to network science presents random graphs as models for real-world networks. Such networks have distinctive empirical properties and a wealth of new models have emerged to capture them. Classroom tested for over ten years, this text places recent advances in a unified framework to enable systematic study. Designed for a master's-level course, where students may only have a basic background in probability, the text covers such important preliminaries as convergence of random variables, probabilistic bounds, coupling, martingales, and branching processes. Building on this base - and motivated by many examples of real-world networks, including the Internet, collaboration networks, and the World Wide Web - it focuses on several important models for complex networks and investigates key properties, such as the connectivity of nodes. Numerous exercises allow students to develop intuition and experience in working with the models.
Published by Cambridge University Press, Cambridge, 2024
ISBN 10: 1107174007 ISBN 13: 9781107174009
Language: English
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Hardcover. Condition: new. Hardcover. Complex networks are key to describing the connected nature of the society that we live in. This book, the second of two volumes, describes the local structure of random graph models for real-world networks and determines when these models have a giant component and when they are small-, and ultra-small, worlds. This is the first book to cover the theory and implications of local convergence, a crucial technique in the analysis of sparse random graphs. Suitable as a resource for researchers and PhD-level courses, it uses examples of real-world networks, such as the Internet and citation networks, as motivation for the models that are discussed, and includes exercises at the end of each chapter to develop intuition. The book closes with an extensive discussion of related models and problems that demonstratemodern approaches to network theory, such as community structure and directed models. The first book to cover local convergence, this text discusses the local and global structure of random graph models for complex networks. Featuring examples of real-world networks for motivation and numerous exercises to build experience, it will be the definitive resource on the subject for researchers and graduate students. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Add to basketBuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This classroom-tested text is the definitive introduction to the mathematics of network science, featuring examples and numerous exercises.
Published by Cambridge University Press, GB, 2016
ISBN 10: 110717287X ISBN 13: 9781107172876
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Add to basketHardback. Condition: New. This rigorous introduction to network science presents random graphs as models for real-world networks. Such networks have distinctive empirical properties and a wealth of new models have emerged to capture them. Classroom tested for over ten years, this text places recent advances in a unified framework to enable systematic study. Designed for a master's-level course, where students may only have a basic background in probability, the text covers such important preliminaries as convergence of random variables, probabilistic bounds, coupling, martingales, and branching processes. Building on this base - and motivated by many examples of real-world networks, including the Internet, collaboration networks, and the World Wide Web - it focuses on several important models for complex networks and investigates key properties, such as the connectivity of nodes. Numerous exercises allow students to develop intuition and experience in working with the models.
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Published by Cambridge University Press, Cambridge, 2016
ISBN 10: 110717287X ISBN 13: 9781107172876
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Add to basketHardcover. Condition: new. Hardcover. This rigorous introduction to network science presents random graphs as models for real-world networks. Such networks have distinctive empirical properties and a wealth of new models have emerged to capture them. Classroom tested for over ten years, this text places recent advances in a unified framework to enable systematic study. Designed for a master's-level course, where students may only have a basic background in probability, the text covers such important preliminaries as convergence of random variables, probabilistic bounds, coupling, martingales, and branching processes. Building on this base - and motivated by many examples of real-world networks, including the Internet, collaboration networks, and the World Wide Web - it focuses on several important models for complex networks and investigates key properties, such as the connectivity of nodes. Numerous exercises allow students to develop intuition and experience in working with the models. Network science is one of the fastest growing areas in science and business. This classroom-tested, self-contained book is designed for master's-level courses and provides a rigorous treatment of random graph models for networks, featuring many examples of real-world networks for motivation and numerous exercises to build intuition and experience. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Published by Cambridge University Press, Cambridge, 2016
ISBN 10: 110717287X ISBN 13: 9781107172876
Language: English
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Add to basketHardcover. Condition: new. Hardcover. This rigorous introduction to network science presents random graphs as models for real-world networks. Such networks have distinctive empirical properties and a wealth of new models have emerged to capture them. Classroom tested for over ten years, this text places recent advances in a unified framework to enable systematic study. Designed for a master's-level course, where students may only have a basic background in probability, the text covers such important preliminaries as convergence of random variables, probabilistic bounds, coupling, martingales, and branching processes. Building on this base - and motivated by many examples of real-world networks, including the Internet, collaboration networks, and the World Wide Web - it focuses on several important models for complex networks and investigates key properties, such as the connectivity of nodes. Numerous exercises allow students to develop intuition and experience in working with the models. Network science is one of the fastest growing areas in science and business. This classroom-tested, self-contained book is designed for master's-level courses and provides a rigorous treatment of random graph models for networks, featuring many examples of real-world networks for motivation and numerous exercises to build intuition and experience. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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ISBN 10: 1107174007 ISBN 13: 9781107174009
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Add to basketBuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This classroom-tested text is the definitive introduction to the mathematics of network science, featuring examples and numerous exercises.
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ISBN 10: 1107174007 ISBN 13: 9781107174009
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Add to basketHardcover. Condition: new. Hardcover. Complex networks are key to describing the connected nature of the society that we live in. This book, the second of two volumes, describes the local structure of random graph models for real-world networks and determines when these models have a giant component and when they are small-, and ultra-small, worlds. This is the first book to cover the theory and implications of local convergence, a crucial technique in the analysis of sparse random graphs. Suitable as a resource for researchers and PhD-level courses, it uses examples of real-world networks, such as the Internet and citation networks, as motivation for the models that are discussed, and includes exercises at the end of each chapter to develop intuition. The book closes with an extensive discussion of related models and problems that demonstratemodern approaches to network theory, such as community structure and directed models. The first book to cover local convergence, this text discusses the local and global structure of random graph models for complex networks. Featuring examples of real-world networks for motivation and numerous exercises to build experience, it will be the definitive resource on the subject for researchers and graduate students. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.