Potential-Based Analysis of Social, Communication, and Distributed Networks [electronic resource] /
By: Etesami, Seyed Rasoul [author.].
Contributor(s): SpringerLink (Online service).
Series: Springer Theses, Recognizing Outstanding Ph.D. Research: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2017Edition: 1st ed. 2017.Description: XXVII, 172 p. 35 illus., 25 illus. in color. | Binding - Card Paper |.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319542898.Subject(s): Computer Engineering
| Communications Engineering, Networks | Mathematics of Algorithmic ComplexityDDC classification: 621.382 Online resources: Click here to access eBook in Springer Nature platform. (Within Campus only.)
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Springer Nature eBookSummary: This work makes major contributions to the thriving area of social, communication, and distributed networks by introducing novel methodologies and tools toward the study of the evolutionary behaviors of these networks, as well as their computational complexity and rates of convergence. By departing from the classical approaches and results in the literature, this work shows that it is possible to handle more complex and realistic nonlinear models where either the traditional approaches fail or lead to weak results. The author also develops several easily implementable algorithms, delivering excellent performance guarantees while running faster than those that exist in the literature. The study undertaken and the approaches adopted enable the analysis of the evolution of several different types of social and distributed networks, with the potential to apply to and resolve several other outstanding issues in such networks.
This work makes major contributions to the thriving area of social, communication, and distributed networks by introducing novel methodologies and tools toward the study of the evolutionary behaviors of these networks, as well as their computational complexity and rates of convergence. By departing from the classical approaches and results in the literature, this work shows that it is possible to handle more complex and realistic nonlinear models where either the traditional approaches fail or lead to weak results. The author also develops several easily implementable algorithms, delivering excellent performance guarantees while running faster than those that exist in the literature. The study undertaken and the approaches adopted enable the analysis of the evolution of several different types of social and distributed networks, with the potential to apply to and resolve several other outstanding issues in such networks.
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