Entropy of dynamical social networks

TL;DR

This paper introduces the entropy measure for dynamical social networks, analyzing phone-call data to reveal daily patterns and behavioral adaptability, highlighting differences from face-to-face interactions.

Contribution

It presents a novel entropy-based framework to quantify information in dynamical social networks and demonstrates its application to real phone-call data revealing daily and behavioral variations.

Findings

Entropy varies with time of day in weekly patterns

Human social behavior shows significant adaptability in interaction durations

Differences in information content between phone and face-to-face interactions

Abstract

Human dynamical social networks encode information and are highly adaptive. To characterize the information encoded in the fast dynamics of social interactions, here we introduce the entropy of dynamical social networks. By analysing a large dataset of phone-call interactions we show evidence that the dynamical social network has an entropy that depends on the time of the day in a typical week-day. Moreover we show evidence for adaptability of human social behavior showing data on duration of phone-call interactions that significantly deviates from the statistics of duration of face-to-face interactions. This adaptability of behavior corresponds to a different information content of the dynamics of social human interactions. We quantify this information by the use of the entropy of dynamical networks on realistic models of social interactions.