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Adaptive Network Dynamics - Modeling and Control of Time-Dependent Social Contacts
Ira B Schwartz1, Leah B Shaw2, Maxim S Shkarayev
1Nonlinear Dynamical Systems Section, Code 6792, US Naval Research Laboratory, Washington, DC 20375 U.S.A.
FUSION ... : Proceedings of the ... International Conference on Information Fusion. International Conference on Information Fusion
|November 22, 2014
Summary
Social networks are dynamic, not static. Understanding adaptive networks helps predict and control phenomena like epidemic spread and recruitment modeling.
Area of Science:
- Network Science
- Dynamical Systems Theory
- Computational Social Science
Background:
- Real-world social networks are not static but exhibit time-dependent connections.
- Individual behaviors and current network structures influence the evolution of social ties.
- Adaptive networks are crucial in understanding phenomena like epidemic spread and social influence.
Purpose of the Study:
- To review the dynamical properties of adaptive networks.
- To demonstrate how these properties predict novel phenomena.
- To provide insights into new control strategies for adaptive network systems.
Main Methods:
- Review of dynamical properties of adaptive networks.
- Analysis of network rewiring based on behavioral decisions.
- Modeling of epidemic outbreaks and recruitment processes.
Main Results:
- Adaptive network dynamics can lead to emergent behaviors not seen in static networks.
- Network rewiring strategies can be optimized for specific outcomes, such as disease containment or recruitment.
- Understanding network adaptability is key to developing effective interventions.
Conclusions:
- Adaptive network models offer valuable insights into complex social phenomena.
- Dynamical properties of these networks are essential for predicting and controlling real-world processes.
- The study highlights potential applications in public health and security modeling.
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