Burstiness and aging in social temporal networks
Antoine Moinet1, Michele Starnini1, Romualdo Pastor-Satorras1
1Departament de Física i Enginyeria Nuclear, Universitat Politècnica de Catalunya, Campus Nord B4, 08034 Barcelona, Spain.
Physical Review Letters
|March 28, 2015
Summary
Burstiness in social networks causes aging effects. Our renewal process model analytically explains this aging and breaks time translation invariance in network degree distributions.
Area of Science:
- Network Science
- Statistical Physics
- Complex Systems
Background:
- Temporal social networks exhibit bursty interaction patterns, characterized by power-law waiting time distributions.
- This burstiness is hypothesized to induce aging effects in time-integrated network representations.
- Understanding these aging effects is crucial for accurately modeling dynamic network evolution.
Purpose of the Study:
- To propose and analyze an analytically tractable model of temporal networks that incorporates burstiness.
- To investigate the emergence of aging phenomena in time-integrated networks due to bursty interactions.
- To analytically derive the topological properties of the resulting integrated network.
Main Methods:
- Development of a renewal process model to simulate agent interactions with bursty waiting times.
- Derivation of an analytical solution for the topological properties of the time-integrated network.
- Validation of the model's predictions using numerical simulations.
- Analysis of an empirical temporal social network to detect aging effects.
Main Results:
- The proposed model successfully reproduces aging effects in the time-integrated network.
- Analytical solutions reveal that the degree distribution of the integrated network loses its time translation invariance.
- Numerical simulations confirm the theoretical predictions regarding network aging and topological properties.
- Empirical analysis provides evidence for burstiness-induced aging in real-world social interactions.
Conclusions:
- Burstiness in temporal social networks is a key driver of aging effects in integrated network structures.
- The developed analytical model offers a framework for understanding and predicting network aging.
- The breaking of time translation invariance in degree distributions is a signature of aging in these networks.
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