Examinando por Autor "Alfaro-Bittner, Karin"

Mostrando 1 - 4 de 4

A compartmental model for cyber-epidemics
(ELSEVIER, 2022-06-17) Aleja, David; Contreras-Aso, Gonzalo; Alfaro-Bittner, Karin; Primo, Eva; Criado, Regino; Romance, Miguel; Boccaletti, Stefano
In our more and more interconnected world, a specific risk is that of a cyber-epidemic (or cyber-pandemic), produced either accidentally or intentionally, where a cyber virus propagates from device to device up to undermining the global Internet system with devastating consequences in terms of economic costs and societal harms related to the shutdown of essential services. We introduce a compartmental model for studying the spreading of a malware and of the awareness of its incidence through different waves which are evolving on top of the same graph structure (the global network of connected devices). This is realized by considering vectorial compartments made of two components, the first being descriptive of the state of the device with respect to the new malware's propagation, and the second accounting for the awareness of the device's user about the presence of the cyber threat. By introducing suitable transition rates between such compartments, one can then follow the evolution of a cyber-epidemic from the moment at which a new virus is seeded in the network, up to when a given user realizes that his/her device has suffered a damage and consequently starts a wave of awareness which eventually ends up with the development of a proper antivirus software. We then compare the overall damage that a malware is able to produce in Erdős-Rényi and scale-free network architectures for both the case in which the virus is causing a fixed damage on each device and the case where, instead, the virus is engineered to mutate while replicating from device to device. Our result constitutes actually the attempt to build a specific compartmental model whose variables and parameters are entirely customized for describing cyber-epidemics.
Implementing and morphing Boolean gates with adaptive synchronization: The case of spiking neurons
(ELSEVIER, 2022-08-19) Yang, Jinling; Primo, Eva; Aleja, David; Criado, Regino; Boccaletti, Stefano; Alfaro-Bittner, Karin
Boolean logic is the paradigm through which modern computation is performed in silica. When nonlinear dynamical systems are interacting in a directed graph, we show that computation abilities emerge spontaneously from adaptive synchronization, which actually can emulate Boolean logic. Precisely, we demonstrate that a single dynamical unit, a spiking neuron modeled by the Hodgkin-Huxley model, can be used as the basic computational unit for realizing all the 16 Boolean logical gates with two inputs and one output, when it is coupled adaptively in a way that depends on the synchronization level between the two input signals. This is realized by means of a set of parameters, whose tuning offers even the possibility of constructing a morphing gate, i.e., a logical gate able to switch efficiently from one to another of such 16 Boolean gates. Extensive simulations demonstrate the efficiency and the accuracy of the proposed computational paradigm.
The transition to synchronization of networked systems
(Springer, 2024-06-10) Bayani, Atiyeh; Nazarimehr, Fahimeh; Jafari, Sajad; Kovalenko, Kirill; Contreras-Aso, Gonzalo; Alfaro-Bittner, Karin; Sánchez-García, Rubén J; Boccaletti, Stefano
We study the synchronization properties of a generic networked dynamical system, and show that, under a suitable approximation, the transition to synchronization can be predicted with the only help of eigenvalues and eigenvectors of the graph Laplacian matrix. The transition comes out to be made of a well defined sequence of events, each of which corresponds to a specific clustered state. The network’s nodes involved in each of the clusters can be identified, and the value of the coupling strength at which the events are taking place can be approximately ascertained. Finally, we present largescale simulations which show the accuracy of the approximation made, and of our predictions in describing the synchronization transition of both synthetic and real-world large size networks, and we even report that the observed sequence of clusters is preserved in heterogeneous networks made of slightly non-identical systems.
Why are there six degrees of separation in a social network?
(American Physical Society, 2023-05-31) Samoylenko, Ivan; Aleja, David; Primo, Eva; Alfaro-Bittner, Karin; Vasilyeva, Ekaterina; Kovalenko, Kirill; Musatov, Daniil; Raigorodskii, Andreii M.; Criado, Regino; Romance, Miguel; Papo, David; Perc, Matjaz; Barzel, Baruch; Boccaletti, Stefano
A wealth of evidence shows that real-world networks are endowed with the small-world property, i.e., that the maximal distance between any two of their nodes scales logarithmically rather than linearly with their size. In addition, most social networks are organized so that no individual is more than six connections apart from any other, an empirical regularity known as the six degrees of separation. Why social networks have this ultrasmall-world organization, whereby the graph’s diameter is independent of the network size over several orders of magnitude, is still unknown. We show that the “six degrees of separation” is the property featured by the equilibrium state of any network where individuals weigh between their aspiration to improve their centrality and the costs incurred in forming and maintaining connections. We show, moreover, that the emergence of such a regularity is compatible with all other features, such as clustering and scale-freeness, that normally characterize the structure of social networks. Thus, our results show how simple evolutionary rules of the kind traditionally associated with human cooperation and altruism can also account for the emergence of one of the most intriguing attributes of social networks.