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Discovering link communities in complex networks by an integer programming model and a genetic algorithm.

Zhenping Li1, Xiang-Sun Zhang2, Rui-Sheng Wang3

  • 1School of Information, Beijing Wuzi University, Beijing, China.

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This study introduces a novel method for identifying overlapping communities in complex networks by proposing a new quantity function and an integer programming model. A genetic algorithm efficiently detects these link communities without predefining their number.

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Area of Science:

  • Complex network analysis
  • Graph theory
  • Computational sociology

Background:

  • Community identification is crucial across disciplines like sociology, biology, and computer science.
  • Communities represent functional subunits within complex systems, often defined by node or link relationships.
  • Existing methods primarily focus on node-based communities, with emerging interest in link-based partitioning for overlapping structures.

Purpose of the Study:

  • To propose a novel quantity function for identifying link communities in complex networks.
  • To formulate the link community partition problem as an integer programming model for overlapping community detection.
  • To develop a genetic algorithm for efficient link community detection without prior knowledge of community count.

Main Methods:

  • Development of a new quantity function for link community identification.
  • Formulation of an integer programming model for partitioning complex networks into overlapping communities.
  • Implementation of a genetic algorithm for detecting link communities in networks.

Main Results:

  • The proposed integer programming model effectively partitions complex networks into overlapping communities.
  • The genetic algorithm demonstrates efficiency in detecting link communities without specifying the number of communities.
  • Validation on both artificial and real-world networks confirms the model and algorithm's effectiveness.

Conclusions:

  • The developed quantity function and integer programming model provide a robust framework for link community identification.
  • The genetic algorithm offers an efficient and flexible approach to detecting overlapping community structures in complex networks.
  • This research advances the understanding and detection of community structures in diverse complex systems.