3 research outputs found
Phase transitions for information diffusion in random clustered networks
We study the conditions for the phase transitions of information diffusion in complex
networks. Using the random clustered network model, a generalisation of the Chung-Lu
random network model incorporating clustering, we examine the effect of clustering under
the Susceptible-Infected-Recovered (SIR) epidemic diffusion model with heterogeneous
contact rates. For this purpose, we exploit the branching process to analyse information
diffusion in random unclustered networks with arbitrary contact rates, and provide novel
iterative algorithms for estimating the conditions and sizes of global cascades,
respectively. Showing that a random clustered network can be mapped into a factor graph,
which is a locally tree-like structure, we successfully extend our analysis to random
clustered networks with heterogeneous contact rates. We then identify the conditions for
phase transitions of information diffusion using our method. Interestingly, for various
contact rates, we prove that random clustered networks with higher clustering coefficients
have strictly lower phase transition points for any given degree sequence. Finally, we
confirm our analytical results with numerical simulations of both synthetically-generated
and real-world networks