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Similarity Forces and Recurrent Components in Human Face-to-Face Interaction Networks
Marco Antonio Rodríguez Flores1, Fragkiskos Papadopoulos1
1Department of Electrical Engineering, Computer Engineering and Informatics, Cyprus University of Technology, 33 Saripolou Street, 3036 Limassol, Cyprus and Social Computing Research Centre (SCRC), Cyprus University of Technology, 3036 Limassol, Cyprus.
Physical Review Letters
|January 5, 2019
Summary
Agents in temporal networks reside in a hidden similarity space, driving social dynamics and recurrent component formation. Ignoring these similarity forces prevents recurrent component formation in interaction networks.
Area of Science:
- Social network analysis
- Complex systems
- Network science
Background:
- Face-to-face interaction networks exhibit recurrent connected components.
- Understanding the underlying social dynamics of these networks is crucial.
Purpose of the Study:
- To explain the formation of recurrent connected components in temporal networks.
- To investigate the role of a hidden similarity space in social dynamics.
Main Methods:
- Modeling agents within a hidden similarity space.
- Analyzing the influence of similarity forces on agent motion and interaction duration.
- Comparing network properties with and without similarity forces.
Main Results:
- The assumption of a hidden similarity space naturally explains recurrent component formation.
- Similarity forces direct agent motion and determine interaction durations.
- Ignoring similarity forces prevents recurrent component formation, though other network properties persist.
Conclusions:
- A hidden similarity space is a key factor in the formation of recurrent structures in temporal interaction networks.
- Social dynamics are significantly influenced by agents' positions and interactions within this hidden space.
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