  Dynamical processes on time-varying complex networks are key to understanding
and modeling a broad variety of processes in socio-technical systems. Here we
focus on empirical temporal networks of human proximity and we aim at
understanding the factors that, in simulation, shape the arrival time
distribution of simple spreading processes. Abandoning the notion of wall-clock
time in favour of node-specific clocks based on activity exposes robust
statistical patterns in the arrival times across different social contexts.
Using randomization strategies and generative models constrained by data, we
show that these patterns can be understood in terms of heterogeneous
inter-event time distributions coupled with heterogeneous numbers of events per
edge. We also show, both empirically and by using a synthetic dataset, that
significant deviations from the above behavior can be caused by the presence of
edge classes with strong activity correlations.
