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October 15, 1999Science36,703 citationsOpen Access

Emergence of Scaling in Random Networks

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ABAlbert-Ĺaszló BarabásiRARéka Albert

Key Points

  • To identify the universal mechanisms responsible for generating scale-free power-law degree distributions across diverse complex networks.
  • Developed a theoretical network growth model incorporating the continuous addition of new vertices over time.
  • Implemented a preferential attachment mechanism where newly added vertices connect with higher probability to nodes that already possess high connectivity.
  • The combination of continuous vertex expansion and preferential attachment self-organized into stationary scale-free power-law distributions matching real-world systems.
  • Generic dynamical rules accounted for complex topology independently of system-specific microscopic details.

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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Cite This Study

Barabási et al. (1999) studied this question.

synapsesocial.com/papers/69d7e8a611d83f35e5ae3254https://doi.org/10.1126/science.286.5439.509
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