Competing memes propagation on networks: A network science perspective

Xuetao Wei, Nicholas C. Valler, B. Aditya Prakash, Iulian Neamtiu, Michalis Faloutsos, Christos Faloutsos

Research output: Contribution to journalArticlepeer-review

75 Scopus citations

Abstract

In this paper, we study the intertwined propagation of two competing memes (or data, rumors, etc.) in a composite network. Within the constraints of this scenario, we ask two key questions: (a) which meme will prevail? and (b) can one influence the outcome of the propagations? Our model is underpinned by two key concepts, a structural graph model (composite network) and a viral propagation model (SI1I2S). Using this framework, we formulate a non-linear dynamic system and perform an eigenvalue analysis to identify the tipping point of the epidemic behavior. Based on insights gained from this analysis, we demonstrate an effective and accurate prediction method to determine viral dominance, which we call the EigenPredictor. Next, using a combination of synthetic and real composite networks, we evaluate the effectiveness of various viral suppression techniques by either a) concurrently suppressing both memes or b) unilaterally suppressing a single meme while leaving the other relatively unaffected.

Original languageEnglish (US)
Article number6517109
Pages (from-to)1049-1060
Number of pages12
JournalIEEE Journal on Selected Areas in Communications
Volume31
Issue number6
DOIs
StatePublished - 2013
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Electrical and Electronic Engineering

Keywords

  • Competition
  • Epidemics
  • Prediction
  • Suppression

Fingerprint

Dive into the research topics of 'Competing memes propagation on networks: A network science perspective'. Together they form a unique fingerprint.

Cite this