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 |
| Event | 2nd Euro-NF Workshop on Network Control and Optimization, NET-COOP 2008 - Paris, France Duration: Sep 8 2008 → Sep 10 2008 |
Keywords
- Models of contagion
- Random graphs
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)