English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

A fundamental question related to innovation diffusion is how the socialnetwork structure influences the process. Empirical evidence regardingreal-world influence networks is very limited. On the other hand, agent-basedmodeling literature reports different and at times seemingly contradictoryresults. In this paper we study innovation diffusion processes for a range ofWatts-Strogatz networks in an attempt to shed more light on this problem. Usingthe so-called Sznajd model as the backbone of opinion dynamics, we find thatthe published results are in fact consistent and allow to predict the role ofnetwork topology in various situations. In particular, the diffusion ofinnovation is easier on more regular graphs, i.e. with a higher clusteringcoefficient. Moreover, in the case of uncertainty - which is particularly highfor innovations connected to public health programs or ecological campaigns - amore clustered network will help the diffusion. On the other hand, when socialinfluence is less important (i.e. in the case of perfect information), ashorter path will help the innovation to spread in the society and - as aresult - the diffusion will be easiest on a random graph.

Rewiring the network. What helps an innovation to diffuse?