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Improving temporal smoothness and snapshot quality in dynamic network community discovery using NOME algorithm.

Lei Cai1, Jincheng Zhou2, Dan Wang3

  • 1State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, China.

Peerj. Computer Science
|August 7, 2023
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Summary
This summary is machine-generated.

We introduce NOME, a novel dynamic community discovery algorithm that uses node occupancy assignment and evolutionary clustering. This method accurately identifies network communities, improving classification and revealing dynamic network changes with a good balance of quality and speed.

Keywords:
Community discoveryDynamic networksEvolutionary clusteringNode occupancy assignment

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Area of Science:

  • Computer Science
  • Network Science
  • Data Mining

Background:

  • Dynamic community discovery is crucial for analyzing evolving network structures and classifying individuals with similar attributes.
  • Accurate classification aids in filtering desired results and understanding dynamic network changes.
  • Existing methods often struggle to balance community detection accuracy with computational efficiency in dynamic networks.

Purpose of the Study:

  • To propose a novel dynamic community discovery algorithm, NOME, that enhances accuracy and efficiency.
  • To leverage node occupancy assignment and multi-objective evolutionary clustering for improved community detection.
  • To provide a method that balances snapshot quality with time cost in dynamic network analysis.

Main Methods:

  • NOME utilizes the MOEA/D framework for multi-objective evolutionary clustering, decomposing modularity and normalized mutual information objectives.
  • A Physarum-based network model initializes populations, with evolution driven by genome matrix crossover and mutation operations.
  • A new node occupancy assignment strategy is introduced, focusing on boundary nodes to improve community division authenticity.

Main Results:

  • Comparative experiments on synthetic and real datasets demonstrate NOME's effectiveness against representative dynamic community detection algorithms.
  • NOME achieves a superior balance between snapshot quality and computational time cost.
  • The node occupancy assignment strategy enhances the accuracy of community division, particularly for nodes at community boundaries.

Conclusions:

  • NOME offers an effective and efficient solution for dynamic community discovery in complex networks.
  • The proposed node occupancy assignment strategy significantly improves the authenticity of community divisions.
  • NOME provides a valuable tool for researchers needing to analyze evolving network structures and perform accurate classifications.