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Diffusion in Networks: Source Locali...

Chen, Zhen.

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Diffusion in Networks: Source Localization, History Reconstruction and Real-time Network Robustification.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Diffusion in Networks: Source Localization, History Reconstruction and Real-time Network Robustification./
Author:	Chen, Zhen.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	163 p.
Notes:	Source: Dissertation Abstracts International, Volume: 79-09(E), Section: B.
Contained By:	Dissertation Abstracts International79-09B(E).
Subject:	Electrical engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10791761
ISBN:	9780355911091

Diffusion in Networks: Source Localization, History Reconstruction and Real-time Network Robustification.
Chen, Zhen.

Diffusion in Networks: Source Localization, History Reconstruction and Real-time Network Robustification. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 163 p.

Source: Dissertation Abstracts International, Volume: 79-09(E), Section: B.

Thesis (Ph.D.)--Arizona State University, 2018.

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 important questions such as the role and the importance of each node in the network for spreading the diffusion and how to top or contain a cascading failure in the network. This dissertation consists of three parts.

ISBN: 9780355911091Subjects--Topical Terms:

649834
Electrical engineering.

Diffusion in Networks: Source Localization, History Reconstruction and Real-time Network Robustification.
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100 1 $a Chen, Zhen. $3 1252694
245 1 0 $a Diffusion in Networks: Source Localization, History Reconstruction and Real-time Network Robustification.
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; Hanghang Tong.
502 $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 important questions such as the role and the importance of each node in the network for spreading the diffusion and how to top or contain a cascading failure in the network. This dissertation consists of three parts.
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 clustering and localization, and proved that for regular trees, the estimators produced by the proposed algorithm are within a constant distance from the real sources with a high probability. Then, we considered the case in which only a partial snapshot is observed and proposed a new algorithm, named Optimal-Jordan-Cover (OJC). The algorithm first extracts a subgraph using a candidate selection algorithm that selects source candidates based on the number of observed infected nodes in their neighborhoods. Then, in the extracted subgraph, OJC finds a set of nodes that "cover" all observed infected nodes with the minimum radius. The set of nodes is called the Jordan cover, and is regarded as the set of diffusion sources. We proved that OJC can locate all sources with probability one asymptotically with partial observations in the Erdos-Renyi (ER) random graph. Multiple experiments on different networks were done, which show our algorithms outperform others.
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. Then we proposed a step-by- step reconstruction algorithm, which can always produce a diffusion history that is consistent with the partial observations. Our experimental results based on synthetic and real networks show that the algorithm significantly outperforms some existing methods.
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 problem is NP-hard, we developed an effective and efficient algorithm, RealWire, to robustify the network and to mitigate the damage during the attack. Extensive experimental results show that our algorithm outperforms other algorithms on most of the robustness metrics.
590 $a School code: 0010.
650 4 $a Electrical engineering. $3 649834
650 4 $a Computer science. $3 523869
650 4 $a Computer engineering. $3 621879
690 $a 0544
690 $a 0984
690 $a 0464
710 2 $a Arizona State University. $b Electrical Engineering. $3 1671741
773 0 $t Dissertation Abstracts International $g 79-09B(E).
790 $a 0010
791 $a Ph.D.
792 $a 2018
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10791761