Marketing in a random network

Leo Hamed Amini, Moez Draief, Marc Lelarge

Research output: Contribution to journalArticle

14 Citations (Scopus)

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
Externally publishedYes

Fingerprint

Random Networks
Marketing
Vertex of a graph
Social Networks
Discrete-time Model
Trigger
Cascade
Continuous Time
Proportion
Customers
Mathematical Model
Mathematical models
Graph in graph theory
Influence
Model

Keywords

  • Models of contagion
  • Random graphs

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Marketing in a random network. / Amini, Leo Hamed; Draief, Moez; Lelarge, Marc.

In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 5425 LNCS, 2009, p. 17-25.

Research output: Contribution to journalArticle

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