Diffusion of innovation in large scale graphs

TL;DR

This paper introduces a new stochastic network model to analyze how innovations like smartphone apps spread in large graphs, emphasizing the roles of network topology, model parameters, and initial conditions in determining success.

Contribution

It proposes a novel stochastic model for innovation diffusion that accounts for persuasion strength increasing with adoption, and provides analytical results for expansive graphs.

Findings

Model predicts conditions for successful diffusion

Analytical results for expansive graph topologies

Numerical simulations support theoretical insights

Abstract

Will a new smartphone application diffuse deeply in the population or will it sink into oblivion soon? To predict this, we argue that common models of spread of innovations based on cascade dynamics or epidemics may not be fully adequate. Therefore we propose a novel stochastic network dynamics modeling the spread of a new technological asset, whose adoption is based on the word-of-mouth and the persuasion strength increases the more the product is diffused. In this paper we carry on an analysis on large scale graphs to show off how the parameters of the model, the topology of the graph and, possibly, the initial diffusion of the asset, determine whether the spread of the asset is successful or not. In particular, by means of stochastic dominations and deterministic approximations, we provide some general results for a large class of expansive graphs. Finally we present numerical simulations trying to expand the analytical results we proved to even more general topologies.