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Modeling the Functional Network for Spatial Navigation in the Human Brain
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Mapping flows on sparse networks with missing links.

Jelena Smiljanić1,2, Daniel Edler1,3,4, Martin Rosvall1

  • 1Integrated Science Lab, Department of Physics, Umeå University, SE-901 87 Umeå, Sweden.

Physical Review. E
|August 16, 2020
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Summary
This summary is machine-generated.

We introduce a Bayesian approach to improve community detection in sparse networks with missing data. This method overcomes overfitting in undersampled networks, revealing significant structures more accurately.

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Area of Science:

  • Complex systems analysis
  • Network science
  • Information theory

Background:

  • Community detection algorithms can overfit unreliable network data, leading to spurious structures.
  • Existing methods struggle with sparse networks containing missing links or undersampled data.
  • Overfitting misrepresents the organization and function of complex systems.

Purpose of the Study:

  • To develop a robust community detection method for sparse networks with missing data.
  • To address the overfitting problem inherent in analyzing undersampled network data.
  • To improve the accuracy of identifying significant structures in complex systems.

Main Methods:

  • Utilized the map equation, a method based on Shannon entropy estimation.
  • Incorporated a Bayesian approach to account for network uncertainties.
  • Applied the enhanced framework to both synthetic and real-world sparse networks.

Main Results:

  • The Bayesian estimate of the map equation effectively mitigates overfitting in undersampled networks.
  • Significant flow-based communities were detected even with missing links.
  • The method demonstrated reliable structure detection in both simulated and empirical network data.

Conclusions:

  • The Bayesian map equation offers a principled solution for community detection in unreliable network data.
  • This approach enhances the understanding of complex system organization and function.
  • Accurate community detection is crucial for interpreting network structures in the presence of data limitations.