No abstract

A complex network in reality often consists of profuse components each of which may suffer from unpredictable perturbations. Because the components of a network could be interdependent, therefore the failures of some components may trigger catastrophes to the whole network. It is thus pivotal to exploit the robustness of complex networks to perturbations. Existing studies on network robustness mainly deal with interdependent or multilayer networks, little work is done to investigate the robustness of multipartite networks which are an indispensable part of complex networks. Here we plumb the robustness of directed multipartite networks. To be specific, we exploit the robustness of bi-directed and unidirectional multipartite networks in face of random node failures. We respectively establish cascading and non-cascading models based on the largest connected component concept for depicting the dynamical processes on bi-directed and unidirectional multipartite networks subject to random node attacks. Based on our developed models, we respectively derive the corresponding percolation theories for mathematicaly computing the robustness of directed multipartite networks to random node failures. We theoretically unravel the first-order and second-order phase transition phenomena on the robustness of directed multipartite networks. The correctness of our developed theories coincide quite well with simulations on computer-generated multipartite networks.

Air traffic is playing a leading role in the global economical growth. Air traffic is indispensable from airport networks which accommodate the traffic demands. Note that airport networks are confronted with intractable uncertainties such as severe meteorological conditions, random mechanical failures of aircraft instruments, terrorist attacks, etc., which give rise to the failures of the components of airport networks. It is of great significance to improve the robustness of airport networks to component failures as the failures can cause staggering economical losses. Existing works either employ network rewire mechanism or add more links to an airport network to enhance the robustness of the given network. In this paper, we provide a counter-intuitive way to enhance the robustness of airport networks. Specifically, we propose to remove links from a given airport network to improve its robustness in face of perturbations. To do so, we develop a single-objective genetic algorithm to locate the links of an airport network whose removal will increase its robustness. Experimental studies on six realworld airport networks validate the feasibility of the proposed research idea. This work provides a new perspective for aviation decision makers to manage airports and air routes, and therefore sheds new light towards robust airspace design.

Airspace capacity overload identification using collision risk patterns

List of Figures 1 Research simulation indicates that the number of conflicts in the airspace increases exponentially with the number of aircraft in the airspace [1]. 2 Overview of the future long-term European traffic (IFR movements) [2]. 3 Annual growth in global air traffic passenger demand between 2006 and