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Rewiring the network. What helps an innovation to diffuse?


  • Katarzyna Sznajd-Weron
  • Janusz Szwabinski
  • Rafal Weron
  • Tomasz Weron


A fundamental question related to innovation diffusion is how the social network structure influences the process. Empirical evidence regarding real-world influence networks is very limited. On the other hand, agent-based modeling literature reports different and at times seemingly contradictory results. In this paper we study innovation diffusion processes for a range of Watts-Strogatz networks in an attempt to shed more light on this problem. Using the so-called Sznajd model as the backbone of opinion dynamics, we find that the published results are in fact consistent and allow to predict the role of network topology in various situations. In particular, the diffusion of innovation is easier on more regular graphs, i.e. with a higher clustering coefficient. Moreover, in the case of uncertainty – which is particularly high for innovations connected to public health programs or ecological campaigns – a more clustered network will help the diffusion. On the other hand, when social influence is less important (i.e. in the case of perfect information), a shorter path will help the innovation to spread in the society and – as a result – the diffusion will be easiest on a random graph.

Suggested Citation

  • Katarzyna Sznajd-Weron & Janusz Szwabinski & Rafal Weron & Tomasz Weron, 2013. "Rewiring the network. What helps an innovation to diffuse?," HSC Research Reports HSC/13/09, Hugo Steinhaus Center, Wroclaw University of Technology.
  • Handle: RePEc:wuu:wpaper:hsc1309

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    File Function: Original version, 2013; Final version published in Journal of Statistical Mechanics P03007 (2014; doi:10.1088/1742-5468/2014/03/P03007)
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    Cited by:

    1. Yibo Lyu & Quanshan Liu & Binyuan He & Jingfei Nie, 2017. "Structural embeddedness and innovation diffusion: the moderating role of industrial technology grouping," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 889-916, May.
    2. Weron, Tomasz & Kowalska-Pyzalska, Anna & Weron, Rafał, 2018. "The role of educational trainings in the diffusion of smart metering platforms: An agent-based modeling approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 591-600.
    3. Anna Kowalska-Pyzalska, 2015. "Social acceptance of green energy and dynamic electricity tariffs - a short review," HSC Research Reports HSC/15/07, Hugo Steinhaus Center, Wroclaw University of Technology.

    More about this item


    Diffusion of innovation; Opinion dynamics; Network structure; Watts-Strogatz network;

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D70 - Microeconomics - - Analysis of Collective Decision-Making - - - General
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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