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Modeling the Functional Network for Spatial Navigation in the Human Brain
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Published on: October 13, 2023

Exploring statistical and population aspects of network complexity.

Frank Emmert-Streib1, Matthias Dehmer

  • 1Computational Biology and Machine Learning Lab, Center for Cancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Queen's University Belfast, Belfast, United Kingdom. v@bio-complexity.com

Plos One
|May 17, 2012
PubMed
Summary

We introduce a new network diversity score (NDS) to quantify network complexity. This score effectively distinguishes ordered, random, and complex networks, outperforming existing measures.

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Area of Science:

  • Network science
  • Complex systems analysis
  • Data quantification

Background:

  • Quantifying object complexity is challenging across disciplines.
  • Existing network complexity measures lack robust categorization capabilities.

Purpose of the Study:

  • Introduce a novel measure, the network diversity score (NDS), for quantifying network structural properties.
  • Enable categorization of networks based on structural complexity.

Main Methods:

  • Developed a composite score (NDS) from four individual network property assessments.
  • Defined the score for a population of networks, not individual ones.
  • Provided a statistical estimator for practical application with finite samples.

Main Results:

  • The NDS effectively distinguishes between ordered, random, and complex networks.
  • The NDS demonstrates superior categorization capabilities compared to 16 other measures.
  • The population-based definition mitigates ambiguity inherent in single-network measures.

Conclusions:

  • The network diversity score offers a robust method for assessing and categorizing network complexity.
  • NDS provides a more nuanced understanding of structural diversity in networks.
  • The statistical estimator facilitates real-world application of NDS.