The impact of network characteristics on the diffusion of innovations

Renana Peres*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

40 Scopus citations


This paper studies the influence of network topology on the speed and reach of new product diffusion. While previous research has focused on comparing network types, this paper explores explicitly the relationship between topology and measurements of diffusion effectiveness. We study simultaneously the effect of three network metrics: the average degree, the relative degree of social hubs (i.e., the ratio of the average degree of highly-connected individuals to the average degree of the entire population), and the clustering coefficient. A novel network-generation procedure based on random graphs with a planted partition is used to generate 160 networks with a wide range of values for these topological metrics. Using an agent-based model, we simulate diffusion on these networks and check the dependence of the net present value (NPV) of the number of adopters over time on the network metrics. We find that the average degree and the relative degree of social hubs have a positive influence on diffusion. This result emphasizes the importance of high network connectivity and strong hubs. The clustering coefficient has a negative impact on diffusion, a finding that contributes to the ongoing controversy on the benefits and disadvantages of transitivity. These results hold for both monopolistic and duopolistic markets, and were also tested on a sample of 12 real networks.

Original languageAmerican English
Pages (from-to)330-343
Number of pages14
JournalPhysica A: Statistical Mechanics and its Applications
StatePublished - 15 May 2014

Bibliographical note

Funding Information:
This paper was supported by the Israel Science Foundation , the Israeli Association for Internet Study , and the Kmart fund . My thanks to Michael Krivelevich for discussion on random graphs with a planted partition. I also thank Meir Karlinsky for his insightful remarks on sampling methods. I thank Zeev Rudnick, Barak Libai and Eitan Muller for their comments.


  • Agent-based models
  • Average degree
  • Clustering
  • Diffusion of innovations
  • Social hubs
  • Word of mouth


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