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      Social stratification in networks: insights from co-authorship networks

      research-article
      1 , , 2 , 1
      Journal of the Royal Society Interface
      The Royal Society
      social stratification, social networks, class stratification

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          Abstract

          It has been observed that real-world social networks often exhibit stratification along economic or other lines, with consequences for class mobility and access to opportunities. With the rise in human interaction data and extensive use of online social networks, the structure of social networks (representing connections between individuals) can be used for measuring stratification. However, although stratification has been studied extensively in the social sciences, there is no single, generally applicable metric for measuring the level of stratification in a network. In this work, we first propose the novel Stratification Assortativity (StA) metric, which measures the extent to which a network is stratified into different tiers. Then, we use the StA metric to perform an in-depth analysis of the stratification of five co-authorship networks. We examine the evolution of these networks over 50 years and show that these fields demonstrate an increasing level of stratification over time, and, correspondingly, the trajectory of a researcher’s career is increasingly correlated with her entry point into the network.

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          Most cited references38

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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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            The Matthew Effect in Science, II: Cumulative Advantage and the Symbolism of Intellectual Property

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              A New Model of Social Class? Findings from the BBC's Great British Class Survey Experiment

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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: ResourcesRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draft
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: ValidationRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Journal
                J R Soc Interface
                J R Soc Interface
                RSIF
                royinterface
                Journal of the Royal Society Interface
                The Royal Society
                1742-5689
                1742-5662
                January 4, 2023
                January 2023
                January 4, 2023
                : 20
                : 198
                : 20220555
                Affiliations
                [ 1 ] Electrical Engineering and Computer Science, Syracuse University, , NY, Syracuse, USA
                [ 2 ] School of Information Studies, Syracuse University, , NY, Syracuse, USA
                Author notes

                Electronic supplementary material is available online at https://doi.org/10.6084/m9.figshare.c.6350496.

                Author information
                http://orcid.org/0000-0003-3583-3750
                Article
                rsif20220555
                10.1098/rsif.2022.0555
                9810428
                36596457
                1dd449fc-e485-4134-8e97-b499e8487144
                © 2023 The Authors.

                Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.

                History
                : August 1, 2022
                : December 8, 2022
                Funding
                Funded by: Center for Hierarchical Manufacturing, National Science Foundation, http://dx.doi.org/10.13039/100006445;
                Award ID: 1908048
                Award ID: 2047224
                Categories
                1004
                69
                Life Sciences–Physics interface
                Research Articles

                Life sciences
                social stratification,social networks,class stratification
                Life sciences
                social stratification, social networks, class stratification

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