Extracting the hierarchical organization of complex systems

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 year

  2007

• Venue

  Proceedings of the National Academy of Sciences of the United States of America

• Publication date

  2007-05-11

• Fields of study

  Biology, Medicine, Physics, Computer Science

• Identifiers
  DOI 10.1073/pnas.0703740104arXiv 0705.1679PMID 17881571PMCID PMC2000510
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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