The switching mechanisms of social network densification

TL;DR

This paper introduces a Bayesian method to distinguish between population change and connection probability shifts as causes of network densification or sparsification, revealing regime switches in social interaction dynamics.

Contribution

The paper develops a novel Bayesian approach to identify and quantify the mechanisms behind network densification and sparsification over time.

Findings

Main mechanism switches between densification and sparsification

Frequency of switches depends on social context

Method uncovers regime-switching in social network dynamics

Abstract

Densification and sparsification of temporal networks are attributed to two fundamental mechanisms: a change in the population in the system and/or a change in the chances that nodes in the system are connected. In theory, each of these mechanisms generates a distinctive type of densification scaling, but in reality both types are generally mixed. Here, we develop a Bayesian statistical method to identify the extent to which each of these mechanisms is at play at a given point in time, taking the mixed densification scaling as input. We apply the method to networks of face-to-face interactions of individuals and reveal that the main mechanism that causes densification and sparsification occasionally switches, the frequency of which depending on the social context. The proposed method uncovers an inherent regime-switching property of network dynamics, which will provide a new insight into the mechanics behind evolving social interactions.