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      Null models for community detection in spatially embedded, temporal networks

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      Journal of Complex Networks

      Oxford University Press (OUP)

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          Social Network Analysis

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            Global trends in emerging infectious diseases

            The next new disease Emerging infectious diseases are a major threat to health: AIDS, SARS, drug-resistant bacteria and Ebola virus are among the more recent examples. By identifying emerging disease 'hotspots', the thinking goes, it should be possible to spot health risks at an early stage and prepare containment strategies. An analysis of over 300 examples of disease emerging between 1940 and 2004 suggests that these hotspots can be accurately mapped based on socio-economic, environmental and ecological factors. The data show that the surveillance effort, and much current research spending, is concentrated in developed economies, yet the risk maps point to developing countries as the more likely source of new diseases. Supplementary information The online version of this article (doi:10.1038/nature06536) contains supplementary material, which is available to authorized users.
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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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                Author and article information

                Journal
                Journal of Complex Networks
                jcomplexnetw
                Oxford University Press (OUP)
                2051-1310
                2051-1329
                August 25 2016
                September 2016
                September 2016
                November 26 2015
                : 4
                : 3
                : 363-406
                Article
                10.1093/comnet/cnv027
                © 2015

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