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      Spatial heterogeneity of ports in the global maritime network detected by weighted ego network analysis

      1 , 2 , 3
      Maritime Policy & Management
      Informa UK Limited

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          Most cited references62

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          Emergence of scaling in random networks

          Systems as diverse as genetic networks or the world wide web are best described as networks with complex topology. A common property of many large networks is that the vertex connectivities follow a scale-free power-law distribution. This feature is found to be a consequence of the two generic mechanisms that networks expand continuously by the addition of new vertices, and new vertices attach preferentially to already well connected sites. A model based on these two ingredients reproduces the observed stationary scale-free distributions, indicating that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.
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            Is Open Access

            Self-similarity of complex networks

            Complex networks have been studied extensively due to their relevance to many real systems as diverse as the World-Wide-Web (WWW), the Internet, energy landscapes, biological and social networks \cite{ab-review,mendes,vespignani,newman,amaral}. A large number of real networks are called ``scale-free'' because they show a power-law distribution of the number of links per node \cite{ab-review,barabasi1999,faloutsos}. However, it is widely believed that complex networks are not {\it length-scale} invariant or self-similar. This conclusion originates from the ``small-world'' property of these networks, which implies that the number of nodes increases exponentially with the ``diameter'' of the network \cite{erdos,bollobas,milgram,watts}, rather than the power-law relation expected for a self-similar structure. Nevertheless, here we present a novel approach to the analysis of such networks, revealing that their structure is indeed self-similar. This result is achieved by the application of a renormalization procedure which coarse-grains the system into boxes containing nodes within a given "size". Concurrently, we identify a power-law relation between the number of boxes needed to cover the network and the size of the box defining a finite self-similar exponent. These fundamental properties, which are shown for the WWW, social, cellular and protein-protein interaction networks, help to understand the emergence of the scale-free property in complex networks. They suggest a common self-organization dynamics of diverse networks at different scales into a critical state and in turn bring together previously unrelated fields: the statistical physics of complex networks with renormalization group, fractals and critical phenomena.
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              Economic networks: the new challenges.

              The current economic crisis illustrates a critical need for new and fundamental understanding of the structure and dynamics of economic networks. Economic systems are increasingly built on interdependencies, implemented through trans-national credit and investment networks, trade relations, or supply chains that have proven difficult to predict and control. We need, therefore, an approach that stresses the systemic complexity of economic networks and that can be used to revise and extend established paradigms in economic theory. This will facilitate the design of policies that reduce conflicts between individual interests and global efficiency, as well as reduce the risk of global failure by making economic networks more robust.
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                Author and article information

                Journal
                Maritime Policy & Management
                Maritime Policy & Management
                Informa UK Limited
                0308-8839
                1464-5254
                June 26 2017
                January 02 2018
                July 03 2017
                January 02 2018
                : 45
                : 1
                : 89-104
                Affiliations
                [1 ] School of Urban and Regional Science, East China Normal University, Shanghai, P.R. China
                [2 ] Faculty of Resource and Environmental Science, Hubei University, Wuhan, P.R. China
                [3 ] Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, P.R. China
                Article
                10.1080/03088839.2017.1345019
                4d0b43e3-b8bc-4656-a4e0-c42296365112
                © 2018
                History

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