Percolation of attack with tunable limited knowledge

Shang, Yilun (2021) Percolation of attack with tunable limited knowledge. Physical Review E, 103 (4). 042316. ISSN 2470-0045

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Percolation models shed a light on network integrity and functionality and have numerous applications in network theory. This paper studies a targeted percolation (αmodel) with incomplete knowledge where the highest degree node in a randomly selected set of n nodes is removed at each step, and the model features a tunable probability that the removed node is instead a random one. A “mirror image” process (β model) in which the target is the lowest degree node is also investigated. We analytically calculate the giant component size, the critical occupation probability, and the scaling law for the percolation threshold with respect to the knowledge level n under both models. We also derive self consistency equations to analyze the k-core organization including the size of the k core and its corona in the context of attacks under tunable limited knowledge. These percolation models are characterized by some interesting critical phenomena and reveal profound quantitative structure discrepancies between Erdős-Rényi networks and power-law networks.

Item Type: Article
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: John Coen
Date Deposited: 06 May 2021 08:00
Last Modified: 31 Jul 2021 16:05

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