Fingerprinting temporal networks of close-range human proximity

Mobile devices and wearable sensors are making available records of human mobility and proximity with unprecedented levels of detail. Here we focus on close-range human proximity networks measured by means of wireless wearable sensors in a variety of real-world environments. We show that simple dynamical processes computed over the time-varying proximity networks can uncover important features of the interaction patterns that go beyond standard statistical indicators of heterogeneity and burstiness, and can tell apart datasets that would otherwise look statistically similar. We show that, due to the intrinsic temporal heterogeneity of human dynamics, the characterization of spreading processes over time-varying networks of human contact may benefit from abandoning the notion of wall-clock time in favor of a node-specific notion of time based on the contact activity of individual nodes.