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      Redrawing the Map of Great Britain from a Network of Human Interactions

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          Abstract

          Do regional boundaries defined by governments respect the more natural ways that people interact across space? This paper proposes a novel, fine-grained approach to regional delineation, based on analyzing networks of billions of individual human transactions. Given a geographical area and some measure of the strength of links between its inhabitants, we show how to partition the area into smaller, non-overlapping regions while minimizing the disruption to each person's links. We tested our method on the largest non-Internet human network, inferred from a large telecommunications database in Great Britain. Our partitioning algorithm yields geographically cohesive regions that correspond remarkably well with administrative regions, while unveiling unexpected spatial structures that had previously only been hypothesized in the literature. We also quantify the effects of partitioning, showing for instance that the effects of a possible secession of Wales from Great Britain would be twice as disruptive for the human network than that of Scotland.

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          Most cited references24

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          Modularity and community structure in networks

          M. Newman (2006)
          Many networks of interest in the sciences, including a variety of social and biological networks, are found to divide naturally into communities or modules. The problem of detecting and characterizing this community structure has attracted considerable recent attention. One of the most sensitive detection methods is optimization of the quality function known as "modularity" over the possible divisions of a network, but direct application of this method using, for instance, simulated annealing is computationally costly. Here we show that the modularity can be reformulated in terms of the eigenvectors of a new characteristic matrix for the network, which we call the modularity matrix, and that this reformulation leads to a spectral algorithm for community detection that returns results of better quality than competing methods in noticeably shorter running times. We demonstrate the algorithm with applications to several network data sets.
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            Detecting community structure in networks

            M. Newman (2004)
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              Geographical dispersal of mobile communication networks

              In this paper, we analyze statistical properties of a communication network constructed from the records of a mobile phone company. The network consists of 2.5 million customers that have placed 810 millions of communications (phone calls and text messages) over a period of 6 months and for whom we have geographical home localization information. It is shown that the degree distribution in this network has a power-law degree distribution \(k^{-5}\) and that the probability that two customers are connected by a link follows a gravity model, i.e. decreases like \(d^{-2}\), where \(d\) is the distance between the customers. We also consider the geographical extension of communication triangles and we show that communication triangles are not only composed of geographically adjacent nodes but that they may extend over large distances. This last property is not captured by the existing models of geographical networks and in a last section we propose a new model that reproduces the observed property. Our model, which is based on the migration and on the local adaptation of agents, is then studied analytically and the resulting predictions are confirmed by computer simulations.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2010
                8 December 2010
                : 5
                : 12
                : e14248
                Affiliations
                [1 ]Senseable City Lab, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
                [2 ]Centre for Advanced Spatial Analysis, University College London, London, United Kingdom
                [3 ]BT Group, Ipswich, United Kingdom
                [4 ]Department of Mathematics, Cornell University, Ithaca, New York, United States of America
                Indiana University, United States of America
                Author notes

                Conceived and designed the experiments: CR S. Sobolevsky FC CA JR MM RC S. Strogatz. Performed the experiments: CR S. Sobolevsky FC. Analyzed the data: CR S. Sobolevsky FC CA JR RC S. Strogatz. Contributed reagents/materials/analysis tools: CR S. Sobolevsky FC MM S. Strogatz. Wrote the paper: CR S. Sobolevsky FC JR S. Strogatz.

                Article
                10-PONE-RA-21553R1
                10.1371/journal.pone.0014248
                2999538
                21170390
                de209c47-61a1-4a1a-b100-56a83c354d2c
                Ratti et al. 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 and source are credited.
                History
                : 23 July 2010
                : 4 November 2010
                Page count
                Pages: 6
                Categories
                Research Article
                Computer Science/Applications
                Mathematics/Algorithms
                Physics/Interdisciplinary Physics

                Uncategorized
                Uncategorized

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