Generalized Jordan Center: A Source Localization Heuristic for Noisy and Incomplete Observations

Huozhi Zhou, Ashish Jagmohan, Lav R. Varshney

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We consider estimating the source of a spreading process on a directed acyclic graph using noisy and incomplete observations; we believe this is the first work on source estimation under noisy information. Our main contribution is a novel heuristic, the generalized Jordan center (GJC), which is the maximum likelihood estimator of the diffusion source under mild conditions.

Original languageEnglish (US)
Title of host publication2019 IEEE Data Science Workshop, DSW 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages243-247
Number of pages5
ISBN (Electronic)9781728107080
DOIs
StatePublished - Jun 2019
Event2019 IEEE Data Science Workshop, DSW 2019 - Minneapolis, United States
Duration: Jun 2 2019Jun 5 2019

Publication series

Name2019 IEEE Data Science Workshop, DSW 2019 - Proceedings

Conference

Conference2019 IEEE Data Science Workshop, DSW 2019
CountryUnited States
CityMinneapolis
Period6/2/196/5/19

Keywords

  • blockchain
  • diffusion source estimation
  • directed acyclic graph
  • message passing

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Computer Networks and Communications
  • Safety, Risk, Reliability and Quality

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  • Cite this

    Zhou, H., Jagmohan, A., & Varshney, L. R. (2019). Generalized Jordan Center: A Source Localization Heuristic for Noisy and Incomplete Observations. In 2019 IEEE Data Science Workshop, DSW 2019 - Proceedings (pp. 243-247). [8755585] (2019 IEEE Data Science Workshop, DSW 2019 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DSW.2019.8755585