The Bass Diffusion Model on Finite Barabasi-Albert Networks

Using a heterogeneous mean-field network formulation of the Bass innovation diffusion model and recent exact results on the degree correlations of Barabasi-Albert networks, we compute the times of the diffusion peak and compare them with those on scale-free networks which have the same scale-free ex...

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Main Authors: M. L. Bertotti, G. Modanese
Format: Article
Language:English
Published: Wiley 2019-01-01
Series:Complexity
Online Access:http://dx.doi.org/10.1155/2019/6352657
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author M. L. Bertotti
G. Modanese
author_facet M. L. Bertotti
G. Modanese
author_sort M. L. Bertotti
collection DOAJ
description Using a heterogeneous mean-field network formulation of the Bass innovation diffusion model and recent exact results on the degree correlations of Barabasi-Albert networks, we compute the times of the diffusion peak and compare them with those on scale-free networks which have the same scale-free exponent but different assortativity properties. We compare our results with those obtained for the SIS epidemic model with the spectral method applied to adjacency matrices. It turns out that diffusion times on finite Barabasi-Albert networks are at a minimum. This may be due to a little-known property of these networks: whereas the value of the assortativity coefficient is close to zero, they look disassortative if one considers only a bounded range of degrees, including the smallest ones, and slightly assortative on the range of the higher degrees. We also find that if the trickle-down character of the diffusion process is enhanced by a larger initial stimulus on the hubs (via a inhomogeneous linear term in the Bass model), the relative difference between the diffusion times for BA networks and uncorrelated networks is even larger, reaching, for instance, the 34% in a typical case on a network with 104 nodes.
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spelling doaj-art-c0adf7fdb59941e594e690c328eeb49c2025-02-03T01:32:57ZengWileyComplexity1076-27871099-05262019-01-01201910.1155/2019/63526576352657The Bass Diffusion Model on Finite Barabasi-Albert NetworksM. L. Bertotti0G. Modanese1Free University of Bolzano-Bozen, Faculty of Science and Technology, 39100 Bolzano, ItalyFree University of Bolzano-Bozen, Faculty of Science and Technology, 39100 Bolzano, ItalyUsing a heterogeneous mean-field network formulation of the Bass innovation diffusion model and recent exact results on the degree correlations of Barabasi-Albert networks, we compute the times of the diffusion peak and compare them with those on scale-free networks which have the same scale-free exponent but different assortativity properties. We compare our results with those obtained for the SIS epidemic model with the spectral method applied to adjacency matrices. It turns out that diffusion times on finite Barabasi-Albert networks are at a minimum. This may be due to a little-known property of these networks: whereas the value of the assortativity coefficient is close to zero, they look disassortative if one considers only a bounded range of degrees, including the smallest ones, and slightly assortative on the range of the higher degrees. We also find that if the trickle-down character of the diffusion process is enhanced by a larger initial stimulus on the hubs (via a inhomogeneous linear term in the Bass model), the relative difference between the diffusion times for BA networks and uncorrelated networks is even larger, reaching, for instance, the 34% in a typical case on a network with 104 nodes.http://dx.doi.org/10.1155/2019/6352657
spellingShingle M. L. Bertotti
G. Modanese
The Bass Diffusion Model on Finite Barabasi-Albert Networks
Complexity
title The Bass Diffusion Model on Finite Barabasi-Albert Networks
title_full The Bass Diffusion Model on Finite Barabasi-Albert Networks
title_fullStr The Bass Diffusion Model on Finite Barabasi-Albert Networks
title_full_unstemmed The Bass Diffusion Model on Finite Barabasi-Albert Networks
title_short The Bass Diffusion Model on Finite Barabasi-Albert Networks
title_sort bass diffusion model on finite barabasi albert networks
url http://dx.doi.org/10.1155/2019/6352657
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