We present a scalable experimental framework for gathering real-time data on face-to-face social interactions with tunable spatial and temporal resolution. We use active Radio Frequency Identification (RFID) devices that assess mutual proximity in a distributed fashion by exchanging low-power radio packets. We show results on the analysis of the dynamical networks of person-to-person interaction obtained in different high- resolution experiments carried out at different orders of magnitude in community size. The developed framework allows for the natural inclusion of the longitudinal dimension in the network description thus going beyond the static network framework. Furthermore, the experimentale framework paves the way for modeling processes both of the network and on the network.
network, RFID, social interaction, causality