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      Emergence of Scaling in Random Networks

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      Science
      American Association for the Advancement of Science (AAAS)

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          Abstract

          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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          Most cited references8

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          Mean-field theory for scale-free random networks

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            Complexity and the economy

            After two centuries of studying equilibria-static patterns that call for no further behavioral adjustments-economists are beginning to study the general emergence of structures and the unfolding of patterns in the economy. When viewed in out-of-equilibrium formation, economic patterns sometimes simplify into the simple static equilibria of standard economics. More often they are ever changing, showing perpetually novel behavior and emergent phenomena. Complexity portrays the economy not as deterministic, predictable, and mechanistic, but as process dependent, organic, and always evolving.
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              Genome evolution. Global methylation in eutherian hybrids.

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                Author and article information

                Journal
                Science
                Science
                American Association for the Advancement of Science (AAAS)
                0036-8075
                1095-9203
                October 15 1999
                October 15 1999
                October 15 1999
                October 15 1999
                : 286
                : 5439
                : 509-512
                Article
                10.1126/science.286.5439.509
                10521342
                4562110e-e899-47c9-8de3-c842dfde0840
                © 1999
                History

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