18
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: not found
      • Article: not found

      Modeling the multi-layer nature of the European Air Transport Network: Resilience and passengers re-scheduling under random failures

      Read this article at

      ScienceOpenPublisher
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Related collections

          Most cited references11

          • Record: found
          • Abstract: found
          • Article: not found

          A simple model of global cascades on random networks.

          The origin of large but rare cascades that are triggered by small initial shocks is a phenomenon that manifests itself as diversely as cultural fads, collective action, the diffusion of norms and innovations, and cascading failures in infrastructure and organizational networks. This paper presents a possible explanation of this phenomenon in terms of a sparse, random network of interacting agents whose decisions are determined by the actions of their neighbors according to a simple threshold rule. Two regimes are identified in which the network is susceptible to very large cascades-herein called global cascades-that occur very rarely. When cascade propagation is limited by the connectivity of the network, a power law distribution of cascade sizes is observed, analogous to the cluster size distribution in standard percolation theory and avalanches in self-organized criticality. But when the network is highly connected, cascade propagation is limited instead by the local stability of the nodes themselves, and the size distribution of cascades is bimodal, implying a more extreme kind of instability that is correspondingly harder to anticipate. In the first regime, where the distribution of network neighbors is highly skewed, it is found that the most connected nodes are far more likely than average nodes to trigger cascades, but not in the second regime. Finally, it is shown that heterogeneity plays an ambiguous role in determining a system's stability: increasingly heterogeneous thresholds make the system more vulnerable to global cascades; but an increasingly heterogeneous degree distribution makes it less vulnerable.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            Evolution of Cooperation in Multiplex Networks

            We study evolutionary game dynamics on structured populations in which individuals take part in several layers of networks of interactions simultaneously. This multiplex of interdependent networks accounts for the different kind of social ties each individual has. By coupling the evolutionary dynamics of a Prisoner's Dilemma game in each of the networks, we show that the resilience of cooperative behaviors for extremely large values of the temptation to defect is enhanced by the multiplex structure. Furthermore, this resilience is intrinsically related to a non-trivial organization of cooperation across the network layers, thus providing a new way out for cooperation to survive in structured populations.
              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Analyzing and modeling real-world phenomena with complex networks: a survey of applications

                Bookmark

                Author and article information

                Journal
                The European Physical Journal Special Topics
                Eur. Phys. J. Spec. Top.
                Springer Science and Business Media LLC
                1951-6355
                1951-6401
                January 2013
                January 29 2013
                January 2013
                : 215
                : 1
                : 23-33
                Article
                10.1140/epjst/e2013-01712-8
                40cc23ae-1171-42e1-a51e-b3d9e4f33325
                © 2013

                http://www.springer.com/tdm

                History

                Comments

                Comment on this article