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Quantifying the compressibility of complex networks.

Christopher W Lynn1,2, Danielle S Bassett3,4,5,6,7,8

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Summary
This summary is machine-generated.

Complex networks, like those in biology, must be compressible to survive. This study models networks as information sources, finding that hierarchical structures with clustering and varied node degrees enhance compressibility.

Keywords:
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Area of Science:

  • Network science
  • Information theory
  • Computational biology

Background:

  • Biological entities rely on complex networks for preservation.
  • These networks must be encoded and replicated under resource constraints.
  • Survival of networks depends on their compressibility.

Purpose of the Study:

  • To define and quantify network compressibility.
  • To identify network features that influence compressibility.
  • To explore the role of compression in network evolution and structure.

Main Methods:

  • Modeling networks as information sources.
  • Applying rate-distortion theory for compression analysis.
  • Analyzing rate-distortion curves to define compressibility.

Main Results:

  • Each network has a unique rate-distortion curve.
  • Network compressibility is defined as the average removable information across scales.
  • Compressibility increases with transitivity (clustering) and degree heterogeneity.

Conclusions:

  • Hierarchical organization, modularity, and heterogeneous degrees facilitate network compression.
  • This framework illuminates the relationship between network structure and compressibility.
  • Compression plays a role in shaping real-world complex networks.