Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/104814
Title: Identifying infection sources and regions in large networks
Authors: Luo, Wuqiong
Tay, Wee Peng
Leng, Mei
Keywords: Infection Graphs
Inference Algorithm
DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2013
Source: Luo, W., Tay, W. P., & Leng, M. (2013). Identifying infection sources and regions in large networks. IEEE Transactions on Signal Processing, 61(11), 2850-2865. doi:10.1109/TSP.2013.2256902
Series/Report no.: IEEE Transactions on Signal Processing
Abstract: Identifying the infection sources in a network, including the index cases that introduce a contagious disease into a population network, the servers that inject a computer virus into a computer network, or the individuals who started a rumor in a social network, plays a critical role in limiting the damage caused by the infection through timely quarantine of the sources. We consider the problem of estimating the infection sources and the infection regions (subsets of nodes infected by each source) in a network, based only on knowledge of which nodes are infected and their connections, and when the number of sources is unknown a priori. We derive estimators for the infection sources and their infection regions based on approximations of the infection sequences count. We prove that if there are at most two infection sources in a geometric tree, our estimator identifies the true source or sources with probability going to one as the number of infected nodes increases. When there are more than two infection sources, and when the maximum possible number of infection sources is known, we propose an algorithm with quadratic complexity to estimate the actual number and identities of the infection sources. Simulations on various kinds of networks, including tree networks, small-world networks and real world power grid networks, and tests on two real data sets are provided to verify the performance of our estimators.
URI: https://hdl.handle.net/10356/104814
http://hdl.handle.net/10220/47851
ISSN: 1053-587X
DOI: 10.1109/TSP.2013.2256902
Rights: © 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TSP.2013.2256902.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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