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Updated: Jul 19, 2026
10:44
Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
Published on: December 7, 2021
Phenomenological models of socioeconomic network dynamics
George C M A Ehrhardt1, Matteo Marsili, Fernando Vega-Redondo
1The Abdus Salam ICTP, Strada Costiera 11, I-34014 Trieste, Italy. gehrhard@ictp.trieste.it
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|October 10, 2006
Summary
This study models social network evolution, revealing how link formation based on node similarity drives phase transitions and hysteresis. Findings show coexistence of high and low connectivity phases, mirroring real-world social networks.
Area of Science:
- Social network analysis
- Computational social science
- Network dynamics
Background:
- Social networks exhibit complex evolution and dynamics.
- Link formation often depends on node similarity.
- Understanding these processes is key to network science.
Purpose of the Study:
- To develop and analyze general models of social network evolution and dynamics.
- To investigate the interplay between network dynamics and evolution.
- To identify emergent phenomena like phase transitions and hysteresis.
Main Methods:
- Development of general models incorporating network dynamics and evolution.
- Numerical simulations for three specific processes.
- Analytic derivations using mean-field approximations.
- Exact solution for a particular network case.
Main Results:
- Interplay between dynamics and evolution leads to phase transitions and hysteresis.
- Coexistence of high and low connectivity phases observed.
- Demonstrated history dependence in network structures.
- Validation through numerical and analytical approaches.
Conclusions:
- The proposed models capture key features of real-world social networks.
- Node similarity-based link formation is a crucial driver of network structure.
- Phase transitions and history dependence are fundamental properties of evolving social networks.
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