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Spontaneous recovery in random hypergraphs
Hao Peng1,2, Zhihao Kuang1, Dandan Zhao1
1School of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, Zhejiang, China.
Chaos (Woodbury, N.Y.)
|July 8, 2024
Summary
This study introduces a new model for dynamic network recovery on hypergraphs, revealing how higher-order interactions improve system resilience. Findings offer insights into designing more robust complex networks.
Area of Science:
- Network Science
- Complex Systems
- Mathematical Modeling
Background:
- Real-world systems, like disaster recovery or financial markets, exhibit spontaneous network activity post-assistance.
- Existing network recovery research primarily focuses on simple networks with pairwise interactions.
- Real-world systems often involve complex, higher-order interactions beyond simple pairs.
Purpose of the Study:
- To propose a novel spontaneous recovery model for complex networks using hypergraphs.
- To investigate dynamic network recovery mechanisms considering higher-order interactions.
- To understand factors influencing network resilience in recovery processes.
Main Methods:
- Developed a spontaneous recovery model applicable to hypergraphs.
- Incorporated two recovery types: internal recovery (independent probabilities) and fast recovery (resource-dependent).
- Analyzed system behavior, including phase transitions and the impact of network properties.
Main Results:
- Observed a phase change in active nodes from continuous to discontinuous as fast recovery conditions eased.
- Demonstrated that increasing average hyperedge cardinality enhances network resilience.
- Found that network heterogeneity positively influences system resilience under higher-order interactions.
Conclusions:
- Higher-order interactions are crucial for understanding complex network recovery.
- Network resilience can be improved by increasing hyperedge cardinality and heterogeneity.
- The proposed model provides essential insights for designing resilient complex systems.
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