Examinando por Autor "Contreras-Aso, Gonzalo"

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.
Can the PageRank centrality be manipulated to obtain any desired ranking?
(AIP Publishing, 2023-08) Contreras-Aso, Gonzalo; Romance, Miguel; Criado, Regino
No se puede subestimar la importancia del algoritmo PageRank en la configuración de la Internet moderna pues, de hecho, sus complejos fundamentos de teoría de redes siguen siendo objeto de investigación. En este artículo realizamos un estudio sistemático de la controlabilidad estructural y paramétrica de los resultados de PageRank, trasladando un problema de teoría de grafos espectral a uno geométrico, en el que surge una caracterización natural de sus clasificaciones. Además,demostramos que el cambio de perspectiva empleado puede aplicarse a la propuesta biplex de PageRank, realizando cálculos numéricos en conjuntos de datos de grafos reales y sintéticos para comparar las medidas de centralidad utilizadas.
Detecting communities in higher-order networks by using their derivative graphs
(Elsevier, 2023-12) Contreras-Aso, Gonzalo; Criado, Regino; Vera, Guillermo; Yang, Jinling
De forma similar a lo que ocurre en el ámbito de los grafos, las comunidades de nodos de un hipergrafo están formadas por grupos de nodos que comparten muchas hiperaristas, de forma que el número de hiperaristas que comparten con el resto de nodos es significativamente menor, por lo que estas comunidades pueden considerarse como compartimentos independientes (o superclusters) del hipergrafo. En este trabajo se presenta un método, basado en el denominado grafo derivado de un hipergrafo, que permite la detección de comunidades de un hipergrafo sin un elevado coste computacional y se presentan varias simulaciones que muestran las importantes ventajas computacionales del método propuesto frente a otros métodos existentes.
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.