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A Network Reduction-Based Multiobjective Evolutionary Algorithm for Community Detection in Large-Scale Complex

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    This study introduces a network reduction method to improve evolutionary algorithms for community detection in large networks. The approach enhances scalability and performance on complex network analysis.

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    Area of Science:

    • Complex Networks Analysis
    • Computational Intelligence
    • Data Mining

    Background:

    • Evolutionary algorithms are effective for community detection in complex networks.
    • Scalability issues arise in large networks due to exponential search space growth.
    • Existing methods struggle with efficiency and performance on large-scale network community detection.

    Purpose of the Study:

    • To propose a network reduction-based multiobjective evolutionary algorithm for large-scale community detection.
    • To address the scalability limitations of evolutionary algorithms in complex network analysis.
    • To improve both computational efficiency and detection performance for large networks.

    Main Methods:

    • Recursively reducing network size during evolutionary process.
    • Identifying local communities as nodes in reduced networks to shrink search space.
    • Implementing a local community repairing strategy to correct misidentified nodes.

    Main Results:

    • Demonstrated superiority over state-of-the-art algorithms on synthetic and real-world networks.
    • Significant improvements in computational efficiency for large-scale network analysis.
    • Enhanced detection performance in identifying communities within complex networks.

    Conclusions:

    • The proposed network reduction strategy effectively enhances evolutionary algorithms for large-scale community detection.
    • The algorithm offers a scalable and high-performing solution for complex network analysis.
    • This method represents a significant advancement in computational approaches to network science.