Models, Entropy and Information of Temporal Social Networks

TL;DR

This paper models the dynamics of temporal social networks, quantifies their information content through entropy, and shows how human behavior modulates this information based on circadian rhythms and technology interfaces.

Contribution

It introduces a reinforcement dynamics model for social interactions and analyzes how human activity patterns influence the information encoded in social network dynamics.

Findings

Reinforcement dynamics effectively model face-to-face and mobile-phone interactions.

Entropy quantifies the information content in temporal social networks.

Human activity modulates network information based on circadian rhythms and technology use.

Abstract

Temporal social networks are characterized by {heterogeneous} duration of contacts, which can either follow a power-law distribution, such as in face-to-face interactions, or a Weibull distribution, such as in mobile-phone communication. Here we model the dynamics of face-to-face interaction and mobile phone communication by a reinforcement dynamics, which explains the data observed in these different types of social interactions. We quantify the information encoded in the dynamics of these networks by the entropy of temporal networks. Finally, we show evidence that human dynamics is able to modulate the information present in social network dynamics when it follows circadian rhythms and when it is interfacing with a new technology such as the mobile-phone communication technology.