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Information-theoretic approach to network modularity.

Etay Ziv1, Manuel Middendorf, Chris H Wiggins

  • 1College of Physicians & Surgeons, Department of Biomedical Engineering, Columbia University, New York, New York 10027, USA.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|May 21, 2005
PubMed
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We developed a novel information-theoretic algorithm for discovering network modules and quantifying network modularity. This approach provides a robust measure applicable to real-world complex networks.

Area of Science:

  • Network science
  • Information theory
  • Computational complexity

Background:

  • Module discovery and network modularity quantification are crucial in understanding complex systems.
  • Existing methods may lack a principled theoretical foundation or broad applicability.

Purpose of the Study:

  • To propose and validate a novel information-theoretic algorithm for network module discovery.
  • To introduce a principled measure of network modularity derived from information theory.
  • To apply the developed algorithm to real-world network data.

Main Methods:

  • Development of an information-theoretic algorithm based on recent advancements.
  • Validation using Monte Carlo generated Erdös-like modular networks.
  • Application of the Network Information Bottleneck (NIB) algorithm to empirical networks.

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Main Results:

  • The proposed algorithm effectively discovers modules and quantifies network modularity.
  • The resulting modularity measure is an order parameter ranging from 0 to 1.
  • Successful application to a social network of scientific co-authorship.

Conclusions:

  • The information-theoretic approach offers a principled and effective method for network analysis.
  • The NIB algorithm provides a valuable tool for uncovering community structures in complex systems.
  • This framework advances the study of network organization and information flow.