On the limits of the intervention on complex systems guided by functional networks

Area of Science:

• Complex systems science
• Network science
• Systems biology

Background:

• Functional networks are widely used to understand complex systems.
• A key assumption is that functional network structure guides interventions on unknown physical networks.

Purpose of the Study:

• To test the assumption that functional network structure can effectively guide interventions on the physical layer.
• To evaluate the performance of this approach across varying network complexities.

Main Methods:

• Simulated a propagation process on a minimal physical network model.
• Used node properties from the corresponding functional network to guide interventions.
• Assessed intervention effectiveness in relation to network topology complexity.

Main Results:

• The effectiveness of functional network-guided interventions decreases with increasing network topology complexity.
• For highly complex networks, such as the European air transport network, the approach offers minimal improvement over random node selection.

Conclusions:

• The assumption that functional networks reliably guide physical interventions is challenged, especially for complex systems.
• Relying solely on functional network properties may be suboptimal for targeted interventions in intricate real-world networks.