Quickest Inference of Susceptible-Infected Cascades in Sparse Networks

Anirudh Sridhar, Tirza Routtenberg, H. Vincent Poor

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We consider the task of estimating a network cascade as fast as possible. The cascade is assumed to spread according to a general Susceptible-Infected process with heterogeneous transmission rates from an unknown source in the network. While the propagation is not directly observable, noisy information about its spread can be gathered through multiple rounds of error-prone diagnostic testing. We propose a novel adaptive procedure which quickly outputs an estimate for the cascade source and the full spread under this observation model. Remarkably, under mild conditions on the network topology, our procedure is able to estimate the full spread of the cascade in an n-vertex network, before poly log n vertices are affected by the cascade. We complement our theoretical analysis with simulation results illustrating the effectiveness of our methods.

Original languageEnglish
Title of host publication2023 IEEE International Symposium on Information Theory, ISIT 2023
PublisherInstitute of Electrical and Electronics Engineers
Pages102-107
Number of pages6
ISBN (Electronic)9781665475549
DOIs
StatePublished - 1 Jan 2023
Event2023 IEEE International Symposium on Information Theory, ISIT 2023 - Taipei, Taiwan, Province of China
Duration: 25 Jun 202330 Jun 2023

Publication series

NameIEEE International Symposium on Information Theory - Proceedings
Volume2023-June
ISSN (Print)2157-8095

Conference

Conference2023 IEEE International Symposium on Information Theory, ISIT 2023
Country/TerritoryTaiwan, Province of China
CityTaipei
Period25/06/2330/06/23

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Information Systems
  • Modeling and Simulation
  • Applied Mathematics

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