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      Deciphering the scientific literature on SDG interactions: A review and reading guide

      , ,
      Science of The Total Environment
      Elsevier BV

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          Modularity and community structure in networks

          M. Newman (2006)
          Many networks of interest in the sciences, including social networks, computer networks, and metabolic and regulatory networks, are found to divide naturally into communities or modules. The problem of detecting and characterizing this community structure is one of the outstanding issues in the study of networked systems. One highly effective approach is the optimization of the quality function known as "modularity" over the possible divisions of a network. Here I show that the modularity can be expressed in terms of the eigenvectors of a characteristic matrix for the network, which I call the modularity matrix, and that this expression leads to a spectral algorithm for community detection that returns results of demonstrably higher quality than competing methods in shorter running times. I illustrate the method with applications to several published network data sets.
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            Finding and evaluating community structure in networks

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              The roads ahead: Narratives for shared socioeconomic pathways describing world futures in the 21st century

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                Author and article information

                Journal
                Science of The Total Environment
                Science of The Total Environment
                Elsevier BV
                00489697
                August 2020
                August 2020
                : 728
                : 138405
                Article
                10.1016/j.scitotenv.2020.138405
                32388023
                8431f943-73c9-463f-9e5f-bd49da4d3957
                © 2020

                https://www.elsevier.com/tdm/userlicense/1.0/

                http://creativecommons.org/licenses/by/4.0/

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