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Sketch-based community detection in evolving networks.

Andre Beckus1, George K Atia1,2

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Summary
This summary is machine-generated.

This study introduces a novel sketch-based algorithm for community detection in dynamic networks. It efficiently identifies network evolution events and handles small clusters, improving computational performance.

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Area of Science:

  • Network Science
  • Computer Science
  • Data Mining

Background:

  • Community detection in networks is crucial for understanding complex systems.
  • Time-varying networks present unique challenges due to their dynamic nature.
  • Existing methods struggle with large-scale networks and small, underrepresented communities.

Purpose of the Study:

  • To develop an efficient algorithm for community detection in time-varying networks.
  • To identify and analyze six key community evolution events (growth, shrinkage, merging, splitting, birth, death).
  • To improve the handling of networks with disproportionately sized clusters.

Main Methods:

  • A novel sketch-based approach is used to represent the essential community structure.
  • The algorithm processes networks exhibiting concurrent community evolution events.
  • A new benchmark based on the stochastic block model is introduced for comprehensive testing.

Main Results:

  • The sketch-based algorithm efficiently handles large networks and reduces computational cost.
  • It effectively identifies six key community evolution events.
  • The approach ensures equal representation of clusters, preventing the loss of small communities.

Conclusions:

  • The proposed sketch-based algorithm offers significant advantages in runtime and accuracy for community detection in dynamic networks.
  • It provides a robust solution for analyzing evolving network structures, especially those with diverse cluster sizes.
  • The new benchmark facilitates thorough evaluation of community detection algorithms.