Modeling human dynamics of face-to-face interaction networks

TL;DR

This paper introduces a simple model that accurately reproduces key features of face-to-face interaction networks, enhancing understanding of human social dynamics and aiding in modeling processes like epidemic spreading.

Contribution

The paper presents a novel, minimalistic model of human face-to-face interactions that captures empirical data features and explains the underlying dynamics.

Findings

Model reproduces distribution of conversation lengths

Captures inter-conversation times and social attraction effects

Provides insights into human interaction dynamics

Abstract

Face-to-face interaction networks describe social interactions in human gatherings, and are the substrate for processes such as epidemic spreading and gossip propagation. The bursty nature of human behavior characterizes many aspects of empirical data, such as the distribution of conversation lengths, of conversations per person, or of inter-conversation times. Despite several recent attempts, a general theoretical understanding of the global picture emerging from data is still lacking. Here we present a simple model that reproduces quantitatively most of the relevant features of empirical face-to-face interaction networks. The model describes agents which perform a random walk in a two dimensional space and are characterized by an attractiveness whose effect is to slow down the motion of people around them. The proposed framework sheds light on the dynamics of human interactions and can improve the modeling of dynamical processes taking place on the ensuing dynamical social networks.