Robustness of hybrid interdependent higher-order networks

Area of Science:

• Network Science
• Complex Systems Analysis
• Cyber-Physical Systems (CPS)

Background:

• Real-world systems exhibit complex interactions, including both pairwise (low-order) and group (higher-order) relationships.
• Existing models often focus on only low-order or higher-order interactions, failing to capture systems with both.
• Cyber-Physical Systems (CPS) exemplify this complexity, with communication (low-order) and physical proximity (higher-order) networks.

Purpose of the Study:

• To develop and analyze a novel hybrid interdependent higher-order network model.
• To incorporate both low-order and higher-order relationships within a unified framework.
• To systematically assess the robustness of this hybrid model against node removal.

Main Methods:

• Proposed a unified theoretical framework for analyzing hybrid interdependent higher-order networks.
• Represented higher-order relationships using hypergraphs.
• Conducted systematic robustness analysis by randomly removing nodes.
• Performed experiments on both artificial and real-world networks with diverse topological structures.

Main Results:

• Theoretical predictions of network robustness showed high consistency with simulation results.
• The hybrid model effectively captures the interplay of low-order and higher-order dependencies.
• Demonstrated the model's applicability across various network topologies.

Conclusions:

• The hybrid interdependent higher-order network model provides a more realistic representation of complex systems.
• Findings deepen the understanding of robustness in systems with mixed interaction types.
• Offers new theoretical insights for enhancing the resilience and failure prevention in interdependent networks.