Recent studies have shown that non-Poisson (“bursty”) behaviors in human interactions can impede the diffusion of information or infectious diseases in social networks. Those studies generally consider models in which nodes are independently active according to the same random timing process, and vary that timing. In reality, people differ widely in the patterns of their activity. In this paper, we develop a simple model of diffusion on networks in which agents can differ in the autocorrelation of their activity patterns. We show that bursty behavior does not always hurt the diffusion, and depending on the features of the environment, having some (but not all) of the population being bursty significantly helps diffusion. Moreover, we prove that in a variety of settings maximizing diffusion requires heterogeneous activity patterns across agents and does not involve any Poisson behavior.
['Mohammad Akbarpour', 'Matthew O. Jackson']
Diffusion in Networks and the Unexpected Virtue of Burstiness