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Complex network comparison based on communicability sequence entropy.

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Summary
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We introduce a new method using communicability sequence entropy to measure differences between complex networks. This approach accurately quantifies structural dissimilarities and aids in network analysis and modeling.

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Area of Science:

  • Network Science
  • Information Theory
  • Complex Systems

Background:

  • Quantifying structural dissimilarities between complex networks is crucial but challenging.
  • Existing methods based on network descriptors often provide limited or incomplete information.
  • A simple and efficient network comparison method is needed.

Purpose of the Study:

  • To develop a novel, accurate, and efficient measure for quantifying structural dissimilarities between complex networks.
  • To introduce a Jensen-Shannon divergence based on communicability sequence entropy as a natural distance measure.
  • To demonstrate the utility of this measure in network analysis and modeling.

Main Methods:

  • Defining communicability sequence entropy based on node communicability.
  • Proposing Jensen-Shannon divergence of communicability sequence entropy as a network distance measure.
  • Conducting extensive experiments on synthetic and evolving networks.

Main Results:

  • The proposed Jensen-Shannon divergence accurately quantifies structural dissimilarities in synthetic networks.
  • The measure successfully identifies the critical percolation probability in evolving random networks.
  • The method effectively guides the selection of appropriate models for simulating real-world systems.

Conclusions:

  • Communicability sequence entropy and Jensen-Shannon divergence offer a powerful new tool for network comparison.
  • This approach overcomes limitations of existing methods, providing more complete information.
  • The measure has practical applications in network science, including model selection and understanding network evolution.