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Diffusion in Networks: Source Localization, History Reconstruction and Real-time Network Robustification
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Diffusion in Networks: Source Localization, History Reconstruction and Real-time Network Robustification. |
| 개인저자 | Chen, Zhen. |
| 단체저자명 | Arizona State University. Electrical Engineering. |
| 발행사항 | [S.l.]: Arizona State University., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 163 p. |
| 기본자료 저록 | Dissertation Abstracts International 79-09B(E). Dissertation Abstract International |
| ISBN | 9780355911091 |
| 학위논문주기 | Thesis (Ph.D.)--Arizona State University, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 79-09(E), Section: B.
Advisers: Lei Ying |
| 요약 | Diffusion processes in networks can be used to model many real-world processes, such as the propagation of a rumor on social networks and cascading failures on power networks. Analysis of diffusion processes in networks can help us answer import |
| 요약 | In the first part, we study the problem of locating multiple diffusion sources in networks under the Susceptible-Infected-Recovered (SIR) model. Given a complete snapshot of the network, we developed a sample-path-based algorithm, named clusteri |
| 요약 | In the second part, we tackle the problem of reconstructing the diffusion history from partial observations. We formulated the diffusion history reconstruction problem as a maximum a posteriori (MAP) problem and proved the problem is NP hard. Th |
| 요약 | In the third part, we consider the problem of improving the robustness of an interdependent network by rewiring a small number of links during a cascading attack. We formulated the problem as a Markov decision process (MDP) problem. While the pr |
| 일반주제명 | Electrical engineering. Computer science. Computer engineering. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
