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Natalie Stanley1, Roland Kwitt2, Marc Niethammer3

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This study introduces a faster method for community detection in large networks by compressing them into super nodes. This approach enhances speed and stability while maintaining accurate group identification, closely matching full network results.

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Area of Science:

  • Network Science
  • Computer Science
  • Data Analysis

Background:

  • Community detection algorithms identify groups in networks based on connectivity.
  • Analyzing large networks with these algorithms can be computationally intensive.
  • Efficient methods are needed to handle the scale of modern complex networks.

Purpose of the Study:

  • To develop a faster and more stable community detection method for large networks.
  • To evaluate a novel approach using a compressed network of 'super nodes'.
  • To assess the performance of this method with standard community detection algorithms.

Main Methods:

  • Recasting large networks into a smaller network of 'super nodes'.
  • Utilizing 'CoreHD' ranking to define super node centers.
  • Applying community detection to the super node network using Louvain algorithm and stochastic block models.

Main Results:

  • Community detection on the super node network is significantly faster.
  • The method produces partitions more aligned with local network connectivity.
  • Results show increased stability across multiple runs and algorithms.
  • Partitions generated using super nodes overlap well with full network results.

Conclusions:

  • Compressing networks into super nodes offers a computationally efficient approach to community detection.
  • This method maintains accuracy and improves stability compared to traditional approaches on large networks.
  • The 'CoreHD' ranking effectively seeds super nodes for robust community detection.