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Fast consensus clustering in complex networks.

Aditya Tandon1, Aiiad Albeshri2, Vijey Thayananthan2

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This study introduces a fast consensus clustering method for large networks. It significantly reduces computational complexity, making community detection feasible for millions of nodes and links.

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Area of Science:

  • Graph theory
  • Network analysis
  • Data mining

Background:

  • Community detection algorithms are often stochastic, yielding variable results.
  • Consensus clustering enhances partition stability and accuracy but is computationally intensive.
  • Current methods struggle with large graphs due to quadratic complexity in consensus matrix calculation.

Purpose of the Study:

  • To develop a computationally efficient consensus clustering algorithm.
  • To enable community detection on large-scale networks.
  • To maintain the accuracy of traditional consensus clustering.

Main Methods:

  • A fast consensus clustering variant is proposed.
  • The consensus matrix is calculated using only graph links and selected node pairs.
  • This approach reduces computational complexity from quadratic to linear.

Main Results:

  • The new method achieves linear time complexity.
  • Performance is comparable to the full consensus clustering technique.
  • The procedure is applicable to networks with millions of nodes and links.

Conclusions:

  • Fast consensus clustering offers a scalable solution for large network analysis.
  • This advancement democratizes advanced community detection techniques for massive datasets.
  • The method preserves accuracy while drastically improving efficiency.