Emergence of Scaling in Random Networks

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 is found to be a consequence of the two generic mechanisms that networks expand continuously by the addition of new vertices, and new vertices attach preferentially to already well connected sites. A model based on these two ingredients reproduces the observed stationary scale-free distributions, indicating that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.

Summary¶

Introduces the Barabasi-Albert model, explaining scale-free degree distributions through network growth and preferential attachment. The paper helped establish power-law degree structure as a major organizing idea in complex networks and gave a compact generative mechanism for hub formation in systems such as the Web and biological networks.

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