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Issue title: 21st RCRA International Workshop on “Experimental evaluation of algorithms for solving problems with combinatorial explosion”
Guest editors: Toni Mancini, Marco Maratea and Francesco Ricca
Article type: Research Article
Authors: Mancini, Luca | Milani, Alfredo | Poggioni, Valentina; * | Chiancone, Andrea
Affiliations: University of Perugia, Perugia, Italy. E-mails: [email protected], [email protected], [email protected], [email protected]
Correspondence: [*] Corresponding author: Valentina Poggioni, University of Perugia, Perugia, Italy. E-mail: [email protected].
Abstract: Immunization strategies are significant in many real scale-free networks, e.g. Internet or communication systems, to prevent virus infections. Several centralized and distributed strategies have been proposed in the last few years. They are efficient but they share the same major limitation: they need to know in advance the network topology and the size of the network or the number of nodes that must be immunized. These requirements make those strategies unsuitable for application to real and dynamic networks. In this paper, we propose an immunization strategy based on distributed autonomous entities that self-regulate their diffusion in the network where they are deployed. Experiments show that the proposed approach produces a population that is able to self regulate in many widely accepted benchmarks while achieving different target coverage rates.
Keywords: Network immunization, scale-free networks, Self Regulating strategy
DOI: 10.3233/AIC-150693
Journal: AI Communications, vol. 29, no. 2, pp. 301-317, 2016
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