US Air Transportation Network Research Articles

An evaluation tool for backbone extraction techniques in weighted complex networks

Networks are essential for analyzing complex systems. However, their growing size necessitates backbone extraction techniques aimed at reducing their size while retaining critical features. In practice, selecting, implementing, and evaluating the most suitable backbone extraction method may be challenging. This paper introduces netbone, a Python package designed for assessing the performance of backbone extraction techniques in weighted networks. Its comparison framework is the standout feature of netbone. Indeed, the tool incorporates state-of-the-art backbone extraction techniques. Furthermore, it provides a comprehensive suite of evaluation metrics allowing users to evaluate different backbones techniques. We illustrate the flexibility and effectiveness of netbone through the US air transportation network analysis. We compare the performance of different backbone extraction techniques using the evaluation metrics. We also show how users can integrate a new backbone extraction method into the comparison framework. netbone is publicly available as an open-source tool, ensuring its accessibility to researchers and practitioners. Promoting standardized evaluation practices contributes to the advancement of backbone extraction techniques and fosters reproducibility and comparability in research efforts. We anticipate that netbone will serve as a valuable resource for researchers and practitioners enabling them to make informed decisions when selecting backbone extraction techniques to gain insights into the structural and functional properties of complex systems.

Node Features Adjusted Stochastic Block Model

Stochastic block model (SBM) and its variants are popular models used in community detection for network data. In this article, we propose a feature-adjusted stochastic block model (FASBM) to capture the impact of node features on the network links as well as to detect the residual community structure beyond that explained by the node features. The proposed model can accommodate multiple node features and estimate the form of feature impacts from the data. Moreover, unlike many existing algorithms that are limited to binary-valued interactions, the proposed FASBM model and inference approaches are easily applied to relational data that generate from any exponential family distribution. We illustrate the methods on simulated networks and on two real-world networks: a brain network and an US air-transportation network.

Governing Competition and Collaboration in Network Industries Using Agent-Based Modeling: A Case-Study of US Air Transportation Network

Network industries include a multitude of organizations and companies delivering valuable services and products for modern life. Surprisingly, the impact of the structural complexity of their underlying physical infrastructure on the behavior of these organizations has not been explored yet. The aim of this paper is to study the role of an initial network structure on the throughput of these complex systems, and to provide evidence for the value of using agent-based modeling (ABM) in governing competition and collaboration in network industries. A two-stage multiround game provides the mathematical foundation to examine the behavior of players in the complex interconnected structure of a network industry. The United States Air Transportation Network is used as the case study. The real data on the different elements of the system are embedded into an agent-based model to provide a descriptive model of behaviors within it. The outcome of the model shows path dependence in the system and highlights the impact of initial network conditions on the market. Moreover, this model provides evidence on the usefulness of ABM for understanding the interrelationships between economic behavior and the physical structure of the system.

Evolutionary simulation of complex networks' structures with specific functional properties

Thorough studies of technological and biological systems have revealed that the inherent networking structures of those systems possess similar topological properties, like node degree distribution or small-world effect, regardless of the context to which those systems are related. Based on that knowledge, there have been numerous attempts to develop models that capture particular topological properties of observed complex networks, although little attention has been paid to developing models with specific functional properties. The present paper proposes a method for the simulation of networks' structures with functional characteristics of interest using a heuristic evolutionary approach and utilizing a Simulated Annealing algorithm. An experimental study is carried out with a US air transportation network and synthetic social networks with known properties.

Evaluation of the resilience of air transportation network with adaptive capacity

ABSTRACTSecuring network resilience of air transportation system is essential to provide a stable level of service as one of major transport modes carrying international passengers and freights. In 2014, about 851 million passengers and 39 billion pounds of freights were delivered by over 9.5 million flights in the United States. As seen in Iceland volcano eruption in 2010, a deficiency of hub airports can bring a huge impact on the whole transport system and even on the world economy. So how the failure of individual node affects the overall network resilience is an important issue to study. Air transportation is known to be a scale-free network, which has few of hubs having high degree. So it is relatively robust against failure but vulnerable to targeted attack on a hub. There are numerous studies devoted to measure node vulnerability and evaluate network robustness; however, previous studies could not consider the node capacity for evaluating overall network performance. This study focuses on the network resilience, where the nodes are located in a real space and have a capacity to function. Using the data from Federal Aviation Administration, the simulation demonstrates and evaluates the resilience of the US air transportation network. This study proposes the indices of adaptative capacity for quantifying network resilience, which represent the ability of a network to replace an attacked node by other adjacent nodes. The simulation has two parts to measure the adaptive capacity of networks: under a single attack and a sustained attack. The results identify the susceptible nodes degrading the adaptive capacity of the network and evaluate each sub-network’s resilience in case of cascading node failures. Therefore, this study can help us to diagnose the vulnerable node and contribute the plan for improvement of network resilience.

Characterization of Delay Propagation in the US Air-Transportation Network

Abstract Complex networks provide a suitable framework to characterize air traffic. Previous works described the world air-transport network as a graph where direct flights are edges and commercial airports are vertices. In this work, we focus instead on the properties of flight delays in the US air- transportation network. We analyze flight performance data in 2010 and study the topological structure of the network as well as the aircraft rotation. The properties of flight delays, including the distribution of total delays, the dependence on the day of the week and the hour-by-hour evolution within each day, are characterized with special attention to flights accumulating delays longer than 12 hours. We find that the distributions are robust to changes in takeoff or landing operations, different moments of the year, or even different airports in the contiguous states. However, airports in remote areas (Hawaii, Alaska, Puerto Rico) can show peculiar distributions biased toward long delays. Additionally, we show that long-delayed flights have an important dependence on the destination airport.

A Heuristic Optimization Method for Mitigating the Impact of a Virus Attack

Taking precautions before or during the start of a virus outbreak can heavily reduce the number of infected. The question which individuals should be immunized in order to mitigate the impact of the virus on the rest of population has received quite some attention in the literature. The dynamics of the of a virus spread through a population is often represented as information spread over a complex network. The strategies commonly proposed to determine which nodes are to be selected for immunization often involve only one centrality measure at a time, while often the topology of the network seems to suggest that a single metric is insufficient to capture the influence of a node entirely.In this work we present a generic method based on a genetic algorithm (GA) which does not rely explicitly on any centrality measures during its search but only exploits this type of information to narrow the search space. The fitness of an individual is defined as the estimated expected number of infections of a virus following SIR dynamics. The proposed method is evaluated on two contact networks: the Goodreau's Faux Mesa high school and the US air transportation network. The GA method manages to outperform the most common strategies based on a single metric for the air transportation network and its performance is comparable with the best performing strategy for the high school network.

Predicting epidemic outbreak from individual features of the spreaders

Knowing which individuals can be more efficient in spreading a pathogen throughout a determinate environment is a fundamental question in disease control. Indeed, over recent years the spread of epidemic diseases and its relationship with the topology of the involved system have been a recurrent topic in complex network theory, taking into account both network models and real-world data. In this paper we explore possible correlations between the heterogeneous spread of an epidemic disease governed by the susceptible–infected–recovered (SIR) model, and several attributes of the originating vertices, considering Erdös–Rényi (ER), Barabási–Albert (BA) and random geometric graphs (RGG), as well as a real case study, the US air transportation network, which comprises the 500 busiest airports in the US along with inter-connections. Initially, the heterogeneity of the spreading is achieved by considering the RGG networks, in which we analytically derive an expression for the distribution of the spreading rates among the established contacts, by assuming that such rates decay exponentially with the distance that separates the individuals. Such a distribution is also considered for the ER and BA models, where we observe topological effects on the correlations. In the case of the airport network, the spreading rates are empirically defined, assumed to be directly proportional to the seat availability. Among both the theoretical and real networks considered, we observe a high correlation between the total epidemic prevalence and the degree, as well as the strength and the accessibility of the epidemic sources. For attributes such as the betweenness centrality and the k-shell index, however, the correlation depends on the topology considered.