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Rumors with Changing Credibility

Authors Charlotte Out , Nicolás Rivera , Thomas Sauerwald , John Sylvester

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Author Details

Charlotte Out
  • Department of Computer Science & Technology, University of Cambridge, UK
Nicolás Rivera
  • Facultad de Ciencias, Universidad de Valparaíso, Chile
Thomas Sauerwald
  • Department of Computer Science & Technology, University of Cambridge, UK
John Sylvester
  • Department of Computer Science, University of Liverpool, UK

Funding

  • Rivera, Nicolás: ANID FONDECYT 3210805 and ANID SIA 85220033.

Cite AsGet BibTex

Charlotte Out, Nicolás Rivera, Thomas Sauerwald, and John Sylvester. Rumors with Changing Credibility. In 15th Innovations in Theoretical Computer Science Conference (ITCS 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 287, pp. 86:1-86:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
https://doi.org/10.4230/LIPIcs.ITCS.2024.86

Abstract

Randomized rumor spreading processes diffuse information on an undirected graph and have been widely studied. In this work, we present a generic framework for analyzing a broad class of such processes on regular graphs. Our analysis is protocol-agnostic, as it only requires the expected proportion of newly informed vertices in each round to be bounded, and a natural negative correlation property. This framework allows us to analyze various protocols, including PUSH, PULL, and PUSH-PULL, thereby extending prior research. Unlike previous work, our framework accommodates message failures at any time t ≥ 0 with a probability of 1-q(t), where the credibility q(t) is any function of time. This enables us to model real-world scenarios in which the transmissibility of rumors may fluctuate, as seen in the spread of "fake news" and viruses. Additionally, our framework is sufficiently broad to cover dynamic graphs.

Subject Classification

ACM Subject Classification
  • Theory of computation → Distributed algorithms
  • Mathematics of computing → Stochastic processes
Keywords
  • Rumor spreading
  • epidemic algorithms
  • "fake news"

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