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Statistical properties and attack tolerance of growing networks with algebraic preferential attachment
Zonghua Liu1, Ying-Cheng Lai, Nong Ye
1Department of Mathematics, Center for Systems Science and Engineering Research, Arizona State University, Tempe 85287, USA.
Summary
Temporal fluctuations in growing networks have minimal impact on properties, but algebraic preferential attachment enhances robustness against failures and attacks compared to scale-free networks.
Area of Science:
- Network Science
- Complex Systems
- Statistical Physics
Background:
- Growing networks are fundamental models in understanding real-world systems.
- Preferential attachment mechanisms explain how highly connected nodes emerge.
- Understanding network robustness is crucial for system resilience.
Purpose of the Study:
- To investigate the impact of temporal fluctuations on network properties under algebraic preferential attachment.
- To assess the tolerance of these networks against random failures and intentional attacks.
- To compare the robustness of algebraic preferential attachment networks with scale-free networks.
Main Methods:
- Derivation of analytical formulas for network connectivity evolution and distribution.
- Conducting numerical simulations to validate theoretical findings.
- Simulating random failures and intentional attacks to evaluate network tolerance.
Main Results:
- Temporal fluctuations generally exhibit minor effects on overall network properties.
- A plateau behavior is observed for small degrees in the connectivity distribution.
- Networks with algebraic preferential attachment demonstrate superior robustness against failures and attacks compared to scale-free networks.
Conclusions:
- Algebraic preferential attachment provides a robust framework for growing networks.
- Network resilience can be significantly enhanced by employing this attachment mechanism.
- The findings offer insights into designing more fault-tolerant complex systems.