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      The Matthew effect in empirical data

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          Abstract

          The Matthew effect describes the phenomenon that in societies, the rich tend to get richer and the potent even more powerful. It is closely related to the concept of preferential attachment in network science, where the more connected nodes are destined to acquire many more links in the future than the auxiliary nodes. Cumulative advantage and success-breads-success also both describe the fact that advantage tends to beget further advantage. The concept is behind the many power laws and scaling behaviour in empirical data, and it is at the heart of self-organization across social and natural sciences. Here, we review the methodology for measuring preferential attachment in empirical data, as well as the observations of the Matthew effect in patterns of scientific collaboration, socio-technical and biological networks, the propagation of citations, the emergence of scientific progress and impact, career longevity, the evolution of common English words and phrases, as well as in education and brain development. We also discuss whether the Matthew effect is due to chance or optimization, for example related to homophily in social systems or efficacy in technological systems, and we outline possible directions for future research.

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          Most cited references44

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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 was found to be a consequence of two generic mechanisms: (i) networks expand continuously by the addition of new vertices, and (ii) new vertices attach preferentially to sites that are already well connected. A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates 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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            NETWORKS OF SCIENTIFIC PAPERS.

            D. Price (1965)
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              Coauthorship networks and patterns of scientific collaboration.

              M. Newman (2004)
              By using data from three bibliographic databases in biology, physics, and mathematics, respectively, networks are constructed in which the nodes are scientists, and two scientists are connected if they have coauthored a paper. We use these networks to answer a broad variety of questions about collaboration patterns, such as the numbers of papers authors write, how many people they write them with, what the typical distance between scientists is through the network, and how patterns of collaboration vary between subjects and over time. We also summarize a number of recent results by other authors on coauthorship patterns.
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                Author and article information

                Journal
                J R Soc Interface
                J R Soc Interface
                RSIF
                royinterface
                Journal of the Royal Society Interface
                The Royal Society
                1742-5689
                1742-5662
                6 September 2014
                6 September 2014
                : 11
                : 98
                : 20140378
                Affiliations
                Faculty of Natural Sciences and Mathematics, University of Maribor , Koroška cesta 160, 2000 Maribor, Slovenia
                Author notes
                Author information
                http://orcid.org/0000-0002-3087-541X
                Article
                rsif20140378
                10.1098/rsif.2014.0378
                4233686
                24990288
                1c2f2443-b98e-4ee8-9771-e87abb83f888

                © 2014 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0/, which permits unrestricted use, provided the original author and source are credited.

                History
                : 10 April 2014
                : 10 June 2014
                Categories
                1004
                16
                120
                Review Articles
                Custom metadata
                September 6, 2014

                Life sciences
                matthew effect,preferential attachment,cumulative advantage,self-organization,power law,empirical data

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