Identifying vital edges in Chinese air route network via memetic algorithm

Due to its rapid development in the past decade, air transportation system has attracted considerable research attention from diverse communities. While most of the previous studies focused on airline networks, here we systematically explore the robustness of the Chinese air route network, and identify the vital edges which form the backbone of Chinese air transportation system. Specifically, we employ a memetic algorithm to minimize the network robustness after removing certain edges hence the solution of this model is the set of vital edges. Counterintuitively, our results show that the most vital edges are not necessarily the edges of highest topological importance, for which we provide an extensive explanation from the microscope of view. Our findings also offer new insights to understanding and optimizing other real-world network systems

Cascades tolerance of scale-free networks with attack cost

Network robustness against cascades is a major topic in the fields of complex networks. In this paper, we propose an attack-cost-based cascading failure model, where the attack cost of nodes is positively related to its degree. We compare four attacking strategies: the random removal strategy (RRS), the low-degree removal strategy (LDRS), the high-degree removal strategy (HDRS) and the genetic algorithm removal strategy (GARS). It is shown that the network robustness against cascades is heavily affected by attack costs and the network exhibits the weakest robustness under GARS. We also explore the relationship between the network robustness and tolerance parameter under these attacking strategies. The simulation results indicate that the critical value of tolerance parameter under GARS is greatly larger than that of other attacking strategies. Our work can supply insight into the robustness and vulnerability of complex networks corresponding to cascading failures.Peer ReviewedPostprint (published version

Articles indexats publicats per investigadors del Campus de Terrassa: 2017

Aquest informe recull els 241 treballs publicats per 222 investigadors/es del Campus de Terrassa en revistes indexades al Journal Citation Report durant el 2017Postprint (published version

Identifying vital edges in Chinese air route network via memetic algorithm

Due to rapid development in the past decade, air transportation system has attracted considerable research attention from diverse communities. While most of the previous studies focused on airline networks, here we systematically explore the robustness of the Chinese air route network, and identify the vital edges which form the backbone of Chinese air transportation system. Specifically, we employ a memetic algorithm to minimize the network robustness after removing certain edges, and hence the solution of this model is the set of vital edges. Counterintuitively, our results show that the most vital edges are not necessarily the edges of the highest topological importance, for which we provide an extensive explanation from the microscope view. Our findings also offer new insights to understanding and optimizing other real-world network systemsPeer Reviewe

Identifying and mitigating security risks for secure and robust NGI networks

Smart city development is important to achieve sustainable cities and societies which help enhance urban services, reduce resource consumption and decrease overall cost. The incorporation of smart cities with the Internet has given us the Next Generation of Internet (NGI) where every smart device exploits the interconnected services and infrastructure of the Internet. The underlying structure of NGI is composed of large scale heterogeneous multilevel systems-of-systems (SoSs) where each system represents a sensor, mobile phone, computer or smart device. Security and privacy is a fundamental requirement of NGI which is heavily dependent on the composition of services and connectivity of the underlying systems. Meaning any unsecure system can affect the security of the entire networked infrastructure/SoSs. Therefore, it is important to analyse and understand the composition of different systems at different levels in NGI in order to identify and mitigate vulnerabilities. This paper proposes a solution to identify and mitigate vulnerabilities within multilevel SoSs, to enhance security without deploying additional security at endpoints, and quantify security levels of individual systems and the entire composed system. The solution was tested and evaluated using simulation and a network testbed. Results show that NGI security can be enhanced with better composition of systems. © 2020 Elsevier Lt

Producció científica de l'ETSEIB a Futur. Articles publicats per investigadors de l'ETSEIB l'any 2017

Postprint (author's final draft

Statistical physics of vaccination

Historically, infectious diseases caused considerable damage to human societies, and they continue to do so today. To help reduce their impact, mathematical models of disease transmission have been studied to help understand disease dynamics and inform prevention strategies. Vaccination–one of the most important preventive measures of modern times–is of great interest both theoretically and empirically. And in contrast to traditional approaches, recent research increasingly explores the pivotal implications of individual behavior and heterogeneous contact patterns in populations. Our report reviews the developmental arc of theoretical epidemiology with emphasis on vaccination, as it led from classical models assuming homogeneously mixing (mean-field) populations and ignoring human behavior, to recent models that account for behavioral feedback and/or population spatial/social structure. Many of the methods used originated in statistical physics, such as lattice and network models, and their associated analytical frameworks. Similarly, the feedback loop between vaccinating behavior and disease propagation forms a coupled nonlinear system with analogs in physics. We also review the new paradigm of digital epidemiology, wherein sources of digital data such as online social media are mined for high-resolution information on epidemiologically relevant individual behavior. Armed with the tools and concepts of statistical physics, and further assisted by new sources of digital data, models that capture nonlinear interactions between behavior and disease dynamics offer a novel way of modeling real-world phenomena, and can help improve health outcomes. We conclude the review by discussing open problems in the field and promising directions for future research