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|Title:||An Algorithmic Framework for Estimating Rumor Sources With Different Start Times||Authors:||Ji, Feng
Tay, Wee Peng
Varshney, Lav R.
|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
|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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