Temporal fidelity in dynamic social networks
It has recently become possible to record detailed social interactions in large social systems with high resolution. As we study these datasets, human social interactions display patterns that emerge at multiple time scales, from minutes to months. On a fundamental level, understanding of the network dynamics can be used to inform the process of measuring social networks. The details of measurement are of particular importance when considering dynamic processes where minute-to-minute details are important, because collection of physical proximity interactions with high temporal resolution is difficult and expensive. Here, we consider the dynamic network of proximity-interactions between approximately 500 individuals participating in the Copenhagen Networks Study. We show that in order to accurately model spreading processes in the network, the dynamic processes that occur on the order of minutes are essential and must be included in the analysis.
- 13.V. Sekara, A. Stopczynski, S. Lehmann, arXiv:1506.04704 (2015)Google Scholar
- 14.L. Wu, B.N. Waber, S. Aral, E. Brynjolfsson, A. Pentland. Mining face-to-face interaction networks using sociometric badges: Predicting productivity in an it configuration task (2008). Available at SSRN 1130251. Google Scholar
- 18.P. Holme, arXiv:1503.06583 (2015) Google Scholar
- 21.J.P. Hansen, A. Alapetite, H.B. Andersen, L. Malmborg, J. Thommesen, in Human-Computer Interaction–INTERACT 2009 (Springer, 2009), pp. 168–181Google Scholar
- 22.J.E. Larsen, P. Sapiezynski, A. Stopczynski, M. Mørup, R. Theodorsen, Crowds, bluetooth, and rock’n’roll: understanding music festival participant behavior, in Proceedings of the 1st ACM international workshop on Personal data meets distributed multimedia (ACM, 2013), pp. 11–18 Google Scholar