Similarity forces and recurrent components in human 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.

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Cited by in corpus (9)

• Dynamic Hidden-Variable Network Models
• Latent geometry and dynamics of proximity networks
• Levy geometric graphs
• Hyperbolic Mapping of Human Proximity Networks
• Metapopulation models imply non-Poissonian statistics of interevent times
• Dynamics of hot random hyperbolic graphs
• On the duration of face-to-face contacts
• Dynamics of cold random hyperbolic graphs with link persistence
• $({\omega }_1,{\omega }_2)$-temporal random hyperbolic graphs