3 research outputs found
Threshold-limited spreading in social networks with multiple initiators
A classical model for social-influence-driven opinion change is the threshold
model. Here we study cascades of opinion change driven by threshold model
dynamics in the case where multiple {\it initiators} trigger the cascade, and
where all nodes possess the same adoption threshold . Specifically, using
empirical and stylized models of social networks, we study cascade size as a
function of the initiator fraction . We find that even for arbitrarily high
value of , there exists a critical initiator fraction beyond
which the cascade becomes global. Network structure, in particular clustering,
plays a significant role in this scenario. Similarly to the case of single-node
or single-clique initiators studied previously, we observe that community
structure within the network facilitates opinion spread to a larger extent than
a homogeneous random network. Finally, we study the efficacy of different
initiator selection strategies on the size of the cascade and the cascade
window