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 language | English (US) |
|---|---|
| Pages (from-to) | 17-25 |
| Number of pages | 9 |
| Journal | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Volume | 5425 LNCS |
| DOIs | |
| State | Published - 2009 |
| Externally published | Yes |
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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 journal › Article
}
TY - JOUR
T1 - Marketing in a random network
AU - Amini, Leo Hamed
AU - Draief, Moez
AU - Lelarge, Marc
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
KW - Models of contagion
KW - Random graphs
UR - http://www.scopus.com/inward/record.url?scp=63449118324&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=63449118324&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-00393-6_3
DO - 10.1007/978-3-642-00393-6_3
M3 - Article
VL - 5425 LNCS
SP - 17
EP - 25
JO - Lecture Notes in Computer Science
JF - Lecture Notes in Computer Science
SN - 0302-9743
ER -