Extracting the hierarchical organization of complex systems

Proc Natl Acad Sci U S A. 2007 Sep 25;104(39):15224-9. doi: 10.1073/pnas.0703740104. Epub 2007 Sep 19.

Abstract

Extracting understanding from the growing "sea" of biological and socioeconomic data is one of the most pressing scientific challenges facing us. Here, we introduce and validate an unsupervised method for extracting the hierarchical organization of complex biological, social, and technological networks. We define an ensemble of hierarchically nested random graphs, which we use to validate the method. We then apply our method to real-world networks, including the air-transportation network, an electronic circuit, an e-mail exchange network, and metabolic networks. Our analysis of model and real networks demonstrates that our method extracts an accurate multiscale representation of a complex system.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Cluster Analysis
  • Escherichia coli / metabolism*
  • Metabolic Networks and Pathways
  • Metabolism
  • Models, Economic
  • Models, Statistical
  • Models, Theoretical
  • Probability
  • Social Class
  • Systems Biology
  • Systems Theory
  • Transportation