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Percolation and Internet Science

Department of Physics and Astronomy and CSDC, University of Florence, via G. Sansone 1, 50019 Sesto Fiorentino, Italy
INFN, sez. Firenze, via G. Sansone 1, 50019 Sesto Fiorentino, Italy
Centre on Cyber-Physical Systems (C2PS), Khalifa University, Saada Street, P.O. Box 127788, Abu Dhabi, United Arab Emirates
HERUS Lab, École Polytechnique Fédérale de Lausanne (EFPL), GR C1 455 (Bâtiment GR)—Station 2, CH-1015 Lausanne, Switzerland
ISI Foundation, via Chisole 5, 10126 Torino, Italy
Instituto de Energás Renovables, Universidad Nacional Autónoma de México, Temixco 62580, Mexico
Author to whom correspondence should be addressed.
Future Internet 2019, 11(2), 35;
Received: 29 December 2018 / Revised: 27 January 2019 / Accepted: 29 January 2019 / Published: 2 February 2019
(This article belongs to the Special Issue 10th Anniversary Feature Papers)
Percolation, in its most general interpretation, refers to the “flow” of something (a physical agent, data or information) in a network, possibly accompanied by some nonlinear dynamical processes on the network nodes (sometimes denoted reaction–diffusion systems, voter or opinion formation models, etc.). Originated in the domain of theoretical and matter physics, it has many applications in epidemiology, sociology and, of course, computer and Internet sciences. In this review, we illustrate some aspects of percolation theory and its generalization, cellular automata and briefly discuss their relationship with equilibrium systems (Ising and Potts models). We present a model of opinion spreading, the role of the topology of the network to induce coherent oscillations and the influence (and advantages) of risk perception for stopping epidemics. The models and computational tools that are briefly presented here have an application to the filtering of tainted information in automatic trading. Finally, we introduce the open problem of controlling percolation and other processes on distributed systems. View Full-Text
Keywords: stochastic processes; networks; risk perception; opinion modeling; epidemic modeling stochastic processes; networks; risk perception; opinion modeling; epidemic modeling
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MDPI and ACS Style

Bagnoli, F.; Bellini, E.; Massaro, E.; Rechtman, R. Percolation and Internet Science. Future Internet 2019, 11, 35.

AMA Style

Bagnoli F, Bellini E, Massaro E, Rechtman R. Percolation and Internet Science. Future Internet. 2019; 11(2):35.

Chicago/Turabian Style

Bagnoli, Franco; Bellini, Emanuele; Massaro, Emanuele; Rechtman, Raúl. 2019. "Percolation and Internet Science" Future Internet 11, no. 2: 35.

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