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Benchmark model to assess community structure in evolving networks.

Clara Granell1, Richard K Darst2, Alex Arenas1,3

  • 1Departament d'Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, 43007 Tarragona, Spain.

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Summary
This summary is machine-generated.

This study introduces a new benchmark for analyzing dynamic network community structures. The model generates oscillating network graphs to test community detection methods over time.

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Area of Science:

  • Network Science
  • Complex Systems Analysis
  • Data Mining

Background:

  • Understanding the temporal evolution of community structure in complex networks is vital for identifying system-wide changes.
  • Current methods either treat network snapshots independently or require complex dynamic procedures, lacking standardized evaluation tools.

Purpose of the Study:

  • To propose a novel benchmark for evaluating dynamic community detection algorithms.
  • To facilitate the assessment of methods designed to capture evolving network structures.

Main Methods:

  • Development of a dynamic benchmark graph generation model based on stochastic block models.
  • Implementation of periodic oscillations in network structure to simulate evolving communities.
  • Extension of existing quality comparison indices for dynamic network analysis.

Main Results:

  • The proposed model generates dynamic benchmark graphs with predictable community structure evolution.
  • The framework allows for systematic testing and comparison of different community detection algorithms in temporal networks.
  • Extended quality indices provide a means to quantitatively assess performance in dynamic scenarios.

Conclusions:

  • The introduced benchmark and extended indices offer a valuable resource for advancing research in dynamic network community detection.
  • This work provides a foundation for developing and validating more robust methods for analyzing evolving complex systems.