Similarity forces and recurrent components in human face-to-face interaction networks

TL;DR

This paper proposes that similarity-based forces in a hidden space explain the formation of recurrent connected components in face-to-face interaction networks, highlighting the importance of underlying social similarity in dynamic network formation.

Contribution

It introduces a model where similarity forces in a hidden space drive the formation of recurrent components, providing a natural explanation for observed social interaction patterns.

Findings

Recurrent components form when similarity forces are included.

Ignoring similarity forces prevents formation of recurrent components.

The model reproduces key properties of face-to-face interaction networks.

Abstract

We show that the social dynamics responsible for the formation of connected components that appear recurrently in face-to-face interaction networks, find a natural explanation in the assumption that the agents of the temporal network reside in a hidden similarity space. Distances between the agents in this space act as similarity forces directing their motion towards other agents in the physical space and determining the duration of their interactions. By contrast, if such forces are ignored in the motion of the agents recurrent components do not form, although other main properties of such networks can still be reproduced.