Marketing in a random network

Hamed Amini, Moez Draief, Marc Lelarge

Research output: Contribution to journalConference articlepeer-review

15 Scopus citations

Abstract

Viral marketing takes advantage of preexisting social networks among customers to achieve large changes in behaviour. Models of influence spread have been studied in a number of domains, including the effect of "word of mouth" in the promotion of new products or the diffusion of technologies. A social network can be represented by a graph where the nodes are individuals and the edges indicate a form of social relationship. The flow of influence through this network can be thought of as an increasing process of active nodes: as individuals become aware of new technologies, they have the potential to pass them on to their neighbours. The goal of marketing is to trigger a large cascade of adoptions. In this paper, we develop a mathematical model that allows to analyze the dynamics of the cascading sequence of nodes switching to the new technology. To this end we describe a continuous-time and a discrete-time models and analyse the proportion of nodes that adopt the new technology over time.

Original languageEnglish (US)
Pages (from-to)17-25
Number of pages9
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5425 LNCS
DOIs
StatePublished - 2009
Event2nd Euro-NF Workshop on Network Control and Optimization, NET-COOP 2008 - Paris, France
Duration: Sep 8 2008Sep 10 2008

Keywords

  • Models of contagion
  • Random graphs

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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