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Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks
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Published on: September 25, 2021

Constant communities in complex networks.

Tanmoy Chakraborty1, Sriram Srinivasan, Niloy Ganguly

  • 1Dept. of Computer Science & Engg., Indian Institute of Technology, Kharagpur, India - 721302.

Scientific Reports
|May 11, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces "constant communities," vertex groups whose network community assignments are stable regardless of vertex order. These invariant groups offer a novel method to improve the reliability of community detection algorithms.

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Area of Science:

  • Network analysis and complex systems science.
  • Computational social science and data mining.

Background:

  • Community detection is crucial for understanding network structures.
  • Current algorithms often yield variable results due to vertex ordering, as optimization is NP-hard.

Purpose of the Study:

  • To identify and characterize vertex groups (constant communities) invariant to vertex ordering in network analysis.
  • To propose metrics for evaluating these constant communities and demonstrate their utility in improving algorithm stability.

Main Methods:

  • Identification of invariant vertex groups (constant communities) within network structures.
  • Development of empirical metrics to quantify the prevalence and properties of constant communities.
  • Application of constant communities as a pre-processing step for community detection algorithms.

Main Results:

  • Discovered invariant groups of vertices (constant communities) whose community assignments are robust to vertex ordering.
  • Proposed novel metrics for evaluating constant communities across different network applications.
  • Demonstrated that using constant communities as a pre-processing step significantly reduces result variation in community detection.

Conclusions:

  • Constant communities represent a fundamental, order-invariant structural property in networks.
  • These invariant groups can serve as a reliable pre-processing technique to enhance the stability of community detection.
  • A case study on phoneme networks reveals constant communities as core functional units within larger community structures.