Rewiring the network. What helps an innovation to diffuse?
Janusz Szwabiński (CoSyNoDy, University of Wrocław, Poland)
A fundamental question related to innovation diffusion is how the
structure of the social network influences the process. Empirical
evidence regarding real-world networks of influence 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 us 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.
References:
- K. Sznajd-Weron, J. Szwabiński, R. Weron, T. Weron (2014) Rewiring the network. What helps an innovation to diffuse?, Journal of Statistical Mechanics P03007 (doi:10.1088/1742-5468/2014/03/P03007)