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Local Community Mining on Distributed and Dynamic Networks From a Multiagent Perspective.

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    This study introduces autonomy-oriented computing for community mining (AOCCM) in large, dynamic networks. The method efficiently detects communities by using reactive agents and modularity gain, reducing computational costs.

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    Area of Science:

    • Computer Science
    • Network Analysis
    • Artificial Intelligence

    Background:

    • Distributed and dynamic networks are prevalent but challenging for traditional community detection due to scale and decentralization.
    • Existing methods often require a global network view, which is frequently unavailable or impractical.

    Purpose of the Study:

    • To propose a novel autonomy-oriented computing-based method for community mining (AOCCM) in distributed environments.
    • To develop an incremental version (AOCCM-i) for dynamic networks.
    • To enable detection of both non-overlapping and overlapping community structures.

    Main Methods:

    • AOCCM employs reactive agents to identify neighborhood nodes with high structural similarity.
    • Community membership is determined by modularity gain.
    • AOCCM-i provides an efficient incremental approach for dynamic network updates.

    Main Results:

    • The proposed methods reduce computational cost by avoiding redundant calculations.
    • High-quality communities are detected in real-life networks.
    • The approach effectively handles decentralized and dynamic network characteristics.

    Conclusions:

    • AOCCM and AOCCM-i offer efficient and scalable solutions for community detection in large, distributed, and dynamic networks.
    • The multiagent perspective and modularity-based approach are effective for decentralized community mining.
    • The methods provide a robust framework for identifying both overlapping and non-overlapping community structures.