  Human communication is commonly represented as a temporal social network, and
evaluated in terms of its uniqueness. We propose a set of new entropy-based
measures for human communication dynamics represented within the temporal
social network as event sequences. Using real world datasets and random
interaction series of different types we find that real human contact events
always significantly differ from random ones. This human distinctiveness
increases over time and by means of the proposed entropy measures, we can
observe sociological processes that take place within dynamic communities.
