Abstract

During the last decade we have witnessed the generalized use of email as a tool for communication in our society. Its use within organizations follows this general trend and a large portion of a company’s internal information flow occurs in this way. Thus, monitoring its use, while preserving the anonymity of its users, is a very valuable tool to know the informal structure of an organization and to compare it with the prescribed formal structure. In particular, we present a community analysis performed on the email network of the Universitat Rovira i Virgili, Tarragona, Spain.

Abstract

transport is one of the key infrastructures of today’s global economy. Connections between airports define airport networks, where nodes are cities served by airports, connected by edges if there is at least one direct flight connecting them. The aims of this research are to relate structural properties of airport networks which explain how these networks respond to isolation of critical nodes, and to gain insight into relevant socio-economic factors that influence the development of airport networks. We split the world airport network (WAN) into seven global region airport networks (GRANs), using the divisions established by OAG database. We gather information about structural properties of each GRAN determining core cities through k-core decomposition, and critical cities through robustness analysis. We find that differences of robustness across GRANs can be explained by the fraction of core cities relative to total cities. Furthermore, analysis of multilevel structure reveal relevant differences between GRANs, rooted on geographical and socio-economic factors, and give insight about how network robustness in airport networks can be enhanced Peer Reviewed Document type: Article

Abstract

We describe an analytical queuing and network decomposition model developed to study the complex phenomenon of the propagation of delays within a large network of major airports. The Approximate Network Delays (AND) model computes the delays due to local congestion at individual airports and captures the " ripple effect" that leads to the propagation of these delays. The model operates by iterating between its two main components: a queuing engine (QE) that computes delays at individual airports and a delay propagation algorithm (DPA) that updates flight schedules and demand rates at all the airports in the model in response to the local delays computed by the QE. The QE is a stochastic and dynamic queuing model that treats each airport in the network as a M(t)/. Ek(t)/1 queuing system. The AND model is very fast computationally, thus making possible the exploration at a macroscopic level of the impacts of a large number of scenarios and policy alternatives on system-wide delays. It has been applied to a network consisting of the 34 busiest airports in the continental United States and provides insights into the interactions through which delays propagate through the network and the often-counterintuitive consequences. Delay propagation tends to " smoothen" daily airport demand profiles and push more demands into late evening hours. Such phenomena are especially evident at hub airports, where some flights may benefit considerably (by experiencing reduced delays) from the changes that occur in the scheduled demand profile as a result of delays and delay propagation. © 2011 Elsevier Ltd.

Abstract

In the invited talk 'Car2x with SDN, NFV and supercomputers' we report about how our past work with SDN [1, 2] allows the design of a smart mobility fieldlab in the huge parking lot the Amsterdam Arena. We explain how we can engineer and test software that handle the complex conditions of the Car2X case. The talk starts by describing the engineering challenges that developers of smart car telecommunications and computing infrastructures face. We concentrate on the development of software defined networks (SDN) that support smart cars optimally and securely over a heterogeneous, dynamic and developing ICT infrastructure. The goal here is to enable smart cars to profit maximally from any bit of information available from fixed and moving objects as well as persons. For example, in a low tech situation, the lack of other options makes that one has to fall back to GPRS to download only traffic jam locations. As a contrast, in an advanced telecom environment video streams from multiple cars are transmitted via 5G pico cells to computers a few millisecond nearby. These computers fuse the video information to generate a local traffic model. In an ultimate situation, cars use all the communications infrastructures that are available including the numerous WiFi hotspots, all generations of mobile telecommunications, the developing car-to-car communications technologies and even the smart phone of a passing person. SDN technologies deal with the complexities of such communication environment. © 2015 IEEE.

Abstract

In complex networks, centrality metrics quantify the connectivity of nodes and identify the most important ones in the transmission of signals. In many real world networks, especially in transportation systems, links are dynamic, i.e. their presence depends on time, and travelling between two nodes requires a non-vanishing time. Additionally, many networks are structured on several layers, representing, e.g., different transportation modes or service providers. Temporal generalisations of centrality metrics based on walk-counting, like Katz centrality, exist, however they do not account for non-zero link travel times and for the multiplex structure. We propose a generalisation of Katz centrality, termed Trip Centrality, counting only the walks that can be travelled according to the network temporal structure, i.e. “trips”, while also differentiating the contributions of inter- and intra-layer walks to centrality. We show an application to the US air transport system, specifically computing airports’ centrality losses due to delays in the flight network. Document type: Article

Abstract

International audience; The dynamical properties of so-called spatial and complex networks are often overlooked in graph theory and network science in general. Container shipping provides a rare example of a global transport network that went through tremendous technological and geographic changes in the last decades or so. This chapter proposes for the first time an empirical analysis of no less than 40 years of inter-port vessel movement data (1977-2016) to describe the evolving properties of the global container shipping network. Main results confirm a number of stylized facts such as the growing size, connectivity, and centralization of this network due to several factors such as economies of scale in liner shipping and the rationalization of related maritime services, the emergence of hub ports, etc. We also provide a new cartography of how had the global container shipping network been geographically distributed over time, thereby highlighting major shifts in terms of port hierarchies and main corridors. We believe that this chapter will contribute to a better understanding of the complex linkages between network structure, technological change, and spatial change, opening the way for new research paths on maritime transport research and network science in general when focusing on evolutionary dynamics.