Measuring network resilience through connection patterns

Networks are at the core of modeling many engineering contexts, mainly in the case of infrastructures and communication systems. The resilience of a network, which is the property of the system capable of absorbing external shocks, is then of paramount relevance in the applications. This paper deals with this topic by advancing a theoretical proposal for measuring the resilience of a network. The proposal is based on the study of the shocks propagation along the patterns of connections among nodes. The theoretical model is tested on the real-world instances of two important airport systems in the US air traffic network; Illinois (including the hub of Chicago) and New York states (with JFK airport).Comment: Keywords: networks; resilience; paths; weighted arcs; air traffic system

Increasing resilience of ATM networks using traffic monitoring and automated anomaly analysis

Systematic network monitoring can be the cornerstone for the dependable operation of safety-critical distributed systems. In this paper, we present our vision for informed anomaly detection through network monitoring and resilience measurements to increase the operators' visibility of ATM communication networks. We raise the question of how to determine the optimal level of automation in this safety-critical context, and we present a novel passive network monitoring system that can reveal network utilisation trends and traffic patterns in diverse timescales. Using network measurements, we derive resilience metrics and visualisations to enhance the operators' knowledge of the network and traffic behaviour, and allow for network planning and provisioning based on informed what-if analysis

MPA network design based on graph network theory and emergent properties of larval dispersal

Despite the recognised effectiveness of networks of Marine Protected Areas (MPAs) as a biodiversity conservation instrument, nowadays MPA network design frequently disregards the importance of connectivity patterns. In the case of sedentary marine populations, connectivity stems not only from the stochastic nature of the physical environment that affects early-life stages dispersal, but also from the spawning stock attributes that affect the reproductive output (e.g., passive eggs and larvae) and its survivorship. Early-life stages are virtually impossible to track in the ocean. Therefore, numerical ocean current simulations coupled to egg and larval Lagrangian transport models remain the most common approach for the assessment of marine larval connectivity. Inferred larval connectivity may be different depending on the type of connectivity considered; consequently, the prioritisation of sites for marine populations' conservation might also differ. Here, we introduce a framework for evaluating and designing MPA networks based on the identification of connectivity hotspots using graph theoretic analysis. We use as a case of study a network of open-access areas and MPAs, off Mallorca Island (Spain), and test its effectiveness for the protection of the painted comber Serranus scriba. Outputs from network analysis are used to: (1) identify critical areas for improving overall larval connectivity; (2) assess the impact of species' biological parameters in network connectivity; and (3) explore alternative MPA configurations to improve average network connectivity. Results demonstrate the potential of graph theory to identify non-trivial egg/larval dispersal patterns and emerging collective properties of the MPA network which are relevant for increasing protection efficiency.Comment: 8 figures, 3 tables, 1 Supplementary material (including 4 table; 3 figures and supplementary methods

Mixing patterns and community structure in networks

Common experience suggests that many networks might possess community structure - division of vertices into groups, with a higher density of edges within groups than between them. Here we describe a new computer algorithm that detects structure of this kind. We apply the algorithm to a number of real-world networks and show that they do indeed possess non-trivial community structure. We suggest a possible explanation for this structure in the mechanism of assortative mixing, which is the preferential association of network vertices with others that are like them in some way. We show by simulation that this mechanism can indeed account for community structure. We also look in detail at one particular example of assortative mixing, namely mixing by vertex degree, in which vertices with similar degree prefer to be connected to one another. We propose a measure for mixing of this type which we apply to a variety of networks, and also discuss the implications for network structure and the formation of a giant component in assortatively mixed networks.Comment: 21 pages, 9 postscript figures, 2 table