Evolution of the social network of scientific collaborations

A. L. Barabási, H. Jeong, Z. Néda, E. Ravasz, A. Schubert, T. Vicsek

Research output: Contribution to journalArticle

1602 Citations (Scopus)


The co-authorship network of scientists represents a prototype of complex evolving networks. In addition, it offers one of the most extensive database to date on social networks. By mapping the electronic database containing all relevant journals in mathematics and neuro-science for an 8-year period (1991-98), we infer the dynamic and the structural mechanisms that govern the evolution and topology of this complex system. Three complementary approaches allow us to obtain a detailed characterization. First, empirical measurements allow us to uncover the topological measures that characterize the network at a given moment, as well as the time evolution of these quantities. The results indicate that the network is scale-free, and that the network evolution is governed by preferential attachment, affecting both internal and external links. However, in contrast with most model predictions the average degree increases in time, and the node separation decreases. Second, we propose a simple model that captures the network's time evolution. In some limits the model can be solved analytically, predicting a two-regime scaling in agreement with the measurements. Third, numerical simulations are used to uncover the behavior of quantities that could not be predicted analytically. The combined numerical and analytical results underline the important role internal links play in determining the observed scaling behavior and network topology. The results and methodologies developed in the context of the co-authorship network could be useful for a systematic study of other complex evolving networks as well, such as the world wide web, Internet, or other social networks.

Original languageEnglish
Pages (from-to)590-614
Number of pages25
JournalPhysica A: Statistical Mechanics and its Applications
Issue number3-4
Publication statusPublished - Aug 15 2002


  • Random networks
  • Scale-free networks
  • Scaling
  • Small-word systems

ASJC Scopus subject areas

  • Statistics and Probability
  • Condensed Matter Physics

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