  Complex networks are used to depict topological features of complex systems.
The structure of a network characterizes the interactions among elements of the
system, and facilitates the study of many dynamical processes taking place on
it. In previous investigations, the topological infrastructure underlying
dynamical systems is simplified as a static and invariable skeleton. However,
this assumption cannot cover the temporal features of many time-evolution
networks, whose components are evolving and mutating. In this letter, utilizing
the log data of WiFi users in a Chinese university campus, we infuse the
temporal dimension into the construction of dynamical human contact network. By
quantitative comparison with the traditional aggregation approach, we find that
the temporal contact network differs in many features, e.g., the reachability,
the path length distribution. We conclude that the correlation between temporal
path length and duration is not only determined by their definitions, but also
influenced by the microdynamical features of human activities under certain
social circumstance as well. The time order of individuals' interaction events
plays a critical role in understanding many dynamical processes via human close
proximity interactions studied in this letter. Besides, our study also provides
a promising measure to identify the potential superspreaders by distinguishing
the nodes functioning as the relay hub.

Physics and Society;Social and Information Networks;Data Analysis, Statistics and Probability