Node Survival in Networks under Correlated Attacks

Area of Science:

• Socio-economic networks
• Complex systems
• Agent-based modeling

Background:

• Understanding network resilience is crucial for socio-economic stability.
• Disaster impacts can be influenced by their spatial and temporal correlations.
• Traditional models often overlook the nuanced effects of correlated disruptions.

Purpose of the Study:

• To investigate how disaster correlation types affect survival in a socio-economic network.
• To analyze the role of network structure in mitigating or exacerbating disaster impacts.
• To introduce and explain the concept of 'disaster masking' within network dynamics.

Main Methods:

• Agent-based numerical simulations of an insurance scheme against disasters.
• Modeling disaster scenarios with uncorrelated, temporal, spatial, and spatio-temporal correlations.
• Analysis of network properties (path length, degree, clustering coefficient) of activated support subsets.

Main Results:

• Survival rates are highly dependent on disaster correlation: spatial and spatio-temporal correlations increase survival, while temporal correlations decrease it.
• Disaster correlation type leads to significant inequality in node survival.
• The concept of 'disaster masking' helps explain observed simulation outcomes.
• Activated network subsets exhibit distinct structural characteristics based on disaster correlation scenarios.

Conclusions:

• Network resilience is not uniform and is critically modulated by the correlation patterns of external shocks.
• Targeted interventions considering disaster correlation may be necessary to ensure equitable network survival.
• The study provides a novel framework for understanding network robustness in the face of correlated risks.