Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/102679
Title: Estimating infection sources in networks using partial timestamps
Authors: Tang, Wenchang
Ji, Feng
Tay, Wee Peng
Keywords: Infection Source
Rumor Source
DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2018
Source: Tang, W., Ji, F., & Tay, W. P. (2018). Estimating infection sources in networks using partial timestamps. IEEE Transactions on Information Forensics and Security, 13(12), 3035-3049. doi:10.1109/TIFS.2018.2837655
Series/Report no.: IEEE Transactions on Information Forensics and Security
Abstract: We study the problem of identifying infection sources in a network based on the network topology, and a subset of infection timestamps. In the case of a single infection source in a tree network, we derive the maximum likelihood estimator of the source and the unknown diffusion parameters. We then introduce a new heuristic involving an optimization over a parametrized family of Gromov matrices to develop a single source estimation algorithm for general graphs. Compared with the breadth-first search tree heuristic commonly adopted in the literature, simulations demonstrate that our approach achieves better estimation accuracy than several other benchmark algorithms, even though these require more information like the diffusion parameters. We next develop a multiple sources estimation algorithm for general graphs, which first partitions the graph into source candidate clusters, and then applies our single source estimation algorithm to each cluster. We show that if the graph is a tree, then each source candidate cluster contains at least one source. Simulations using synthetic and real networks, and experiments using real-world data suggest that our proposed algorithms are able to estimate the true infection source(s) to within a small number of hops with a small portion of the infection timestamps being observed.
URI: https://hdl.handle.net/10356/102679
http://hdl.handle.net/10220/47788
ISSN: 1556-6013
DOI: 10.1109/TIFS.2018.2837655
Rights: © 2018 Institute of Electrical and Electronics Engineers (IEEE). All rights reserved. This paper was published in IEEE Transactions on Information Forensics and Security and is made available with permission of Institute of Electrical and Electronics Engineers (IEEE).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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