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Decomposition-based multiobjective evolutionary algorithm for community detection in dynamic social networks.

Jingjing Ma1, Jie Liu1, Wenping Ma1

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Summary
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This study introduces a new algorithm for analyzing dynamic social networks. It effectively identifies community structures and their evolution by balancing snapshot quality and temporal cost.

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Area of Science:

  • Network Science
  • Data Mining
  • Social Network Analysis

Background:

  • Community structure is a key property in social networks.
  • Analyzing dynamic networks requires balancing snapshot quality and temporal cost.
  • Existing methods struggle to optimize both community partition quality and temporal evolution simultaneously.

Purpose of the Study:

  • To propose a novel decomposition-based multiobjective algorithm for community detection in dynamic networks.
  • To simultaneously optimize snapshot quality and temporal cost for accurate community structure and evolution analysis.
  • To reveal the evolving community structure in dynamic networks.

Main Methods:

  • Employs a multiobjective evolutionary algorithm based on decomposition (MOEA/D).
  • Optimizes modularity (for snapshot quality) and normalized mutual information (for temporal cost).
  • Incorporates a problem-specific local search strategy to enhance effectiveness.

Main Results:

  • The proposed algorithm effectively identifies community structures and captures their evolution.
  • Demonstrates superior accuracy and steadiness compared to two existing algorithms in experiments.
  • Validated on both computer-generated and real-world dynamic networks.

Conclusions:

  • The decomposition-based multiobjective approach is effective for dynamic community detection.
  • Simultaneous optimization of quality and temporal cost leads to more accurate insights into network evolution.
  • The algorithm provides a robust and reliable method for analyzing dynamic social networks.