Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/81429
Title: An Algorithmic Framework for Estimating Rumor Sources With Different Start Times
Authors: Ji, Feng
Tay, Wee Peng
Varshney, Lav R.
Keywords: Rumor source
Infection source
Issue Date: 2017
Source: Ji, F., Tay, W. P., & Varshney, L. R. (2017). An Algorithmic Framework for Estimating Rumor Sources With Different Start Times. IEEE Transactions on Signal Processing, 65(10), 2517-2530.
Series/Report no.: IEEE Transactions on Signal Processing
Abstract: We study the problem of identifying multiple rumor or infection sources in a network under the susceptible-infected model, and where these sources may start infection spreading at different times. We introduce the notion of an abstract estimator that, given the infection graph, assigns a higher value to each vertex in the graph it considers more likely to be a rumor source. This includes several of the single-source estimators developed in the literature. We introduce the concepts of a quasi-regular tree and a heavy center, which allows us to develop an algorithmic framework that transforms an abstract estimator into a two-source joint estimator, in which the infection graph can be thought of as covered by overlapping infection regions. We show that our algorithm converges to a local optimum of the estimation function if the underlying network is a quasi-regular tree. We further extend our algorithm to more than two sources, and heuristically to general graphs. Simulation results on both synthetic and real-world networks suggest that our algorithmic framework outperforms several existing multiple-source estimators, which typically assume that all sources start infection spreading at the same time.
URI: https://hdl.handle.net/10356/81429
http://hdl.handle.net/10220/43464
ISSN: 1053-587X
DOI: 10.1109/TSP.2017.2659643
Rights: © 2017 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: [http://dx.doi.org/10.1109/TSP.2017.2659643].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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