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      Global Spatio-Temporal Patterns in Human Migration: A Complex Network Perspective

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          Abstract

          Migration is a powerful adaptive strategy for humans to navigate hardship and pursue a better quality of life. As a universal vehicle facilitating exchanges of ideas, culture, money and goods, international migration is a major contributor to globalization. Consisting of countries linked by multiple connections of human movements, global migration constitutes a network. Despite the important role of human migration in connecting various communities in different parts of the world, the topology and behavior of the international migration network and its changes through time remain poorly understood. Here we show that the global human migration network became more interconnected during the latter half of the twentieth century and that migrant destination choice partly reflects colonial and postcolonial histories, language, religion, and distances. From 1960 to 2000 we found a steady increase in network transitivity (i.e. connectivity between nodes connected to the same node), a decrease in average path length and an upward shift in degree distribution, all of which strengthened the ‘small-world’ behavior of the migration network. Furthermore, we found that distinct groups of countries preferentially interact to form migration communities based largely on historical, cultural and economic factors.

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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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            Finding Statistically Significant Communities in Networks

            Community structure is one of the main structural features of networks, revealing both their internal organization and the similarity of their elementary units. Despite the large variety of methods proposed to detect communities in graphs, there is a big need for multi-purpose techniques, able to handle different types of datasets and the subtleties of community structure. In this paper we present OSLOM (Order Statistics Local Optimization Method), the first method capable to detect clusters in networks accounting for edge directions, edge weights, overlapping communities, hierarchies and community dynamics. It is based on the local optimization of a fitness function expressing the statistical significance of clusters with respect to random fluctuations, which is estimated with tools of Extreme and Order Statistics. OSLOM can be used alone or as a refinement procedure of partitions/covers delivered by other techniques. We have also implemented sequential algorithms combining OSLOM with other fast techniques, so that the community structure of very large networks can be uncovered. Our method has a comparable performance as the best existing algorithms on artificial benchmark graphs. Several applications on real networks are shown as well. OSLOM is implemented in a freely available software (http://www.oslom.org), and we believe it will be a valuable tool in the analysis of networks.
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              The new economics of labour migration and the role of remittances in the migration process.

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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2013
                23 January 2013
                : 8
                : 1
                : e53723
                Affiliations
                [1 ]Department of Environmental Sciences, University of Virginia, Charlottesville, Virginia, United States of America
                [2 ]Department of Environmental, Land, and Infrastructure Engineering, Politecnico di Torino, Turin, Italy
                University of Namur, Belgium
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: PD KFD FL LR. Analyzed the data: KFD FL LR. Wrote the paper: PD KFD FL LR.

                Article
                PONE-D-12-27959
                10.1371/journal.pone.0053723
                3553122
                23372664
                b3b39175-8b65-43df-8794-579181d9a53e
                Copyright @ 2013

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 13 September 2012
                : 4 December 2012
                Page count
                Pages: 8
                Funding
                Research was funded by University of Virginia. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Earth Sciences
                Geography
                Human Geography
                Behavioral Geography
                Mathematics
                Applied Mathematics
                Complex Systems
                Social and Behavioral Sciences
                Economics
                Human Capital
                Economics of Migration
                Geography
                Human Geography
                Cultural Geography
                Settlement Patterns
                Spatial Analysis
                Psychology
                Behavior
                Sociology
                Culture
                Social Networks

                Uncategorized
                Uncategorized

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