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

Activity clocks: spreading dynamics on temporal networks of human contact