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Diffusion processes in large networks have been used to model many real-world phenomena, including how rumors spread on the Internet, epidemics among human beings, emotional contagion through social networks, and even gene regulatory processes. Fundamental estimation principles and efficient algorithms for locating diffusion sources can answer a wide range of important questions, such as identifying the source of a widely spread rumor on online social networks. This book provides an overview of recent progress on source localization in large networks, focusing on theoretical principles and fundamental limits. The book covers both discrete-time diffusion models and continuous-time diffusion models. For discrete-time diffusion models, the book focuses on the Jordan infection center; for continuous-time diffusion models, it focuses on the rumor center. Most theoretical results on source localization are based on these two types of estimators or their variants. This book also includes algorithms that leverage partial-time information for source localization and a brief discussion of interesting unresolved problems in this area.
| Autorius: | Kai Zhu, Lei Ying, |
| Serija: | Synthesis Lectures on Learning, Networks, and Algorithms |
| Leidėjas: | Springer Nature Switzerland |
| Išleidimo metai: | 2018 |
| Knygos puslapių skaičius: | 96 |
| ISBN-10: | 303179284X |
| ISBN-13: | 9783031792847 |
| Formatas: | 235 x 191 x 6 mm. Knyga minkštu viršeliu |
| Kalba: | Anglų |
Parašykite atsiliepimą apie „Diffusion Source Localization in Large Networks“
