Entropy Measures of Human Communication Dynamics

TL;DR

This paper introduces new entropy-based metrics to analyze human communication dynamics within temporal social networks, revealing that real human interactions are distinct from random patterns and evolve over time, reflecting underlying sociological processes.

Contribution

The paper presents novel entropy measures for analyzing human communication in temporal networks, enabling the detection of sociological processes and differences from random interaction series.

Findings

Real human contact events significantly differ from random series.

Human communication distinctiveness increases over time.

Entropy measures reveal sociological processes within communities.

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.