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020 ▼a 9780355911091
035 ▼a (MiAaPQ)AAI10791761
035 ▼a (MiAaPQ)asu:17680
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0820 ▼a 621.3
1001 ▼a Chen, Zhen.
24510 ▼a Diffusion in Networks: Source Localization, History Reconstruction and Real-time Network Robustification.
260 ▼a [S.l.] : ▼b Arizona State University., ▼c 2018
260 1 ▼a Ann Arbor : ▼b ProQuest Dissertations & Theses, ▼c 2018
300 ▼a 163 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 79-09(E), Section: B.
500 ▼a Advisers: Lei Ying
5021 ▼a Thesis (Ph.D.)--Arizona State University, 2018.
520 ▼a 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
520 ▼a 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
520 ▼a 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
520 ▼a 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
590 ▼a School code: 0010.
650 4 ▼a Electrical engineering.
650 4 ▼a Computer science.
650 4 ▼a Computer engineering.
690 ▼a 0544
690 ▼a 0984
690 ▼a 0464
71020 ▼a Arizona State University. ▼b Electrical Engineering.
7730 ▼t Dissertation Abstracts International ▼g 79-09B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0010
791 ▼a Ph.D.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T14997662 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자