Abstract
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.
Highlights
Despite living in social communities and witnessing people communicate, at the first glance, we may not recognize clear patterns or trends of dynamic changes in communication – the general impression may be that people interact almost randomly
It was possible to define a number of natural subsequent measures, such as temporal connectedness[5] between nodes representing the reachability from the source node to destination node in a given time, temporal diameter as a largest temporal distance between any two nodes or characteristic temporal path length that defines the temporal distance over all pairs of nodes[7]
We compute entropy values for four different dataset with data of real human interactions: (1) face-to-face meetings at HyperText conference, (2) text messages exchanged between students for six semesters (NetSense), (3) email communications in the manufacturing company, and (4) face-to-face interactions between patients and hospital staff members
Summary
Despite living in social communities and witnessing people communicate, at the first glance, we may not recognize clear patterns or trends of dynamic changes in communication – the general impression may be that people interact almost randomly. It was possible to define a number of natural subsequent measures, such as temporal connectedness[5] between nodes representing the reachability from the source node to destination node in a given time, temporal diameter as a largest temporal distance between any two nodes or characteristic temporal path length that defines the temporal distance over all pairs of nodes[7] Another important aspect of time-varying networks is the interevent time distribution[8] that defines the frequency of events; it can be used to verify how bursty is the behavior in a given network. An event sequence is a time ordered list of interactions between pairs of individuals/agents within a given social group
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