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Percolation of attack with tunable limited knowledge.

¹Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, United Kingdom.

Physical Review. E

|May 19, 2021

Summary

This summary is machine-generated.

This study introduces targeted percolation models (α and β) for network analysis. These models reveal how network structure impacts integrity under targeted attacks with limited knowledge.

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Area of Science:

Network theory
Statistical physics
Complex systems

Background:

Percolation models are crucial for understanding network integrity and functionality.
Real-world networks often face targeted attacks, necessitating models that account for incomplete knowledge of network structure.

Purpose of the Study:

To investigate targeted percolation models (α and β) with incomplete knowledge.
To analytically determine the impact of limited knowledge on network integrity and k-core organization.
To compare network structure discrepancies between Erdős-Rényi and power-law networks under these models.

Main Methods:

Analytical calculation of giant component size and critical occupation probability.
Derivation of self-consistency equations for k-core analysis.
Investigation of percolation threshold scaling with knowledge level (n).

Main Results:

The study analytically calculates key percolation metrics under limited knowledge for both α and β models.
Self-consistency equations reveal insights into k-core organization during attacks.
Significant quantitative structure differences are identified between Erdős-Rényi and power-law networks.

Conclusions:

Targeted percolation models with incomplete knowledge offer valuable insights into network robustness.
The α and β models demonstrate critical phenomena and highlight network structure vulnerabilities.
Findings underscore the importance of network topology in resilience against targeted attacks.