Hidden dependence of spreading vulnerability on topological complexity.

Area of Science:

• Complex Systems Science
• Network Science
• Dynamical Processes

Background:

• Spreading phenomena in complex systems occur on dynamic temporal networks.
• Quantifying a system's susceptibility to spreading (spreading vulnerability) from network topology is challenging.
• Existing methods struggle to compare spreading vulnerability across diverse complex systems.

Purpose of the Study:

• To develop a novel, parameter-free metric for measuring the topological complexity of temporal networks.
• To establish a quantitative link between temporal network topology and spreading vulnerability.
• To enable cross-system comparisons of spreading potential.

Main Methods:

• Developed the "entropy of temporal entanglement" metric.
• Utilized data from diverse real-world complex systems.
• Simulated three types of stochastic dynamical spreading processes on temporal networks.

Main Results:

• The entropy of temporal entanglement quantifies topological complexity in temporal networks.
• This metric allows for parameter-free comparisons across different complex systems.
• The entropy of temporal entanglement accurately predicts spreading vulnerability irrespective of the spreading process dynamics.

Conclusions:

• The entropy of temporal entanglement is a robust measure of spreading vulnerability in temporal networks.
• This entropic measure offers a unified approach to understanding spreading dynamics across various systems.
• Potential applications span natural, social, biological, and engineered domains.