AST: Activity-Security-Trust driven modeling of time varying networks

Area of Science:

• Complex Systems Science
• Network Science
• Mathematical Modeling

Background:

• Traditional network modeling often focuses on explicit factors like agent activity.
• Recent network evolution is influenced by both explicit (activity) and implicit (security, trust) factors.
• Existing activity-driven models may not fully capture the complexity of time-varying networks.

Purpose of the Study:

• To propose a novel network model that incorporates both explicit and implicit driving forces.
• To develop the Activity-Security-Trust (AST) driven model for analyzing time-varying networks.
• To investigate the impact of security and trust on network evolution dynamics.

Main Methods:

• Development of the Activity-Security-Trust (AST) driven model.
• Synthetic consideration of explicit (activity) and implicit (security, trust) driving forces.
• Analysis of time-dependent trade-offs in agent connection decisions.

Main Results:

• The AST-driven model more accurately captures highly dynamical network behaviors.
• The model facilitates a profound understanding of how security and trust influence network evolution.
• It mitigates biases introduced by models relying solely on activity representations.

Conclusions:

• Integrating security and trust provides a more comprehensive approach to modeling complex network evolution.
• The AST model offers improved accuracy in analyzing dynamic network processes.
• This framework advances the study of intrinsic driving forces in time-varying networks.