Random Graphs and Complex Networks: Volume 2 (Hardcover)

Remco van der Hofstad

ISBN 10: 1107174007 / ISBN 13: 9781107174009

Editore: Cambridge University Press, Cambridge, 2024

Riguardo questo articolo

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 Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Codice articolo 9781107174009

Dati bibliografici

• Titolo: Random Graphs and Complex Networks: Volume 2 (Hardcover)
• Editore: Cambridge University Press, Cambridge
• Data di pubblicazione: 2024
• Lingua: Inglese
• ISBN 10: 1107174007
• ISBN 13: 9781107174009
• Rilegatura: Hardcover
• Condizione: new

Descrizione articolo

Riassunto

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.