Marketing in Random Networks

TL;DR

This paper develops mathematical models to analyze how influence spreads through social networks during viral marketing campaigns, focusing on the dynamics of technology adoption cascades.

Contribution

It introduces continuous-time and discrete-time models to analyze influence spread and adoption dynamics in social networks.

Findings

Models describe the proportion of adopters over time

Analysis of influence cascade dynamics

Comparison of continuous and discrete models

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.