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    This study introduces a new algorithm, MOEA-SA_OV, for detecting overlapping communities in attributed networks by combining topological structure and node attributes. The method effectively identifies meaningful overlapping community structures in both directed and undirected networks.

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    Area of Science:

    • Network science
    • Data mining
    • Computational intelligence

    Background:

    • Real-world networks often possess both structural connections and node attributes, termed attributed networks.
    • Existing community detection methods primarily focus on separated communities, struggling with overlapping structures in attributed networks.
    • Overlapping communities are a crucial characteristic of complex networks, necessitating advanced detection techniques.

    Purpose of the Study:

    • To propose a novel multiobjective evolutionary algorithm (MOEA-SA_OV) for overlapping community detection in attributed networks.
    • To effectively integrate both topological structure and node attributes for improved community detection.
    • To develop a method capable of automatically determining the number of communities.

    Main Methods:

    • A multiobjective evolutionary algorithm (MOEA-SA_OV) framework based on attribute similarity.
    • Incorporation of a modified extended modularity (EQ_OV) as the first objective, handling directed and undirected networks.
    • Utilization of attribute similarity (S_A) as the second objective, alongside a novel encoding/decoding strategy for overlapping communities.
    • Implementation within the nondominated sorting genetic algorithm II (NSGA-II) framework.

    Main Results:

    • The MOEA-SA_OV algorithm successfully identifies Pareto fronts of overlapping community structures.
    • The method demonstrates effectiveness in both synthetic and real-world attributed networks.
    • Experimental validation confirms the practical significance of the detected overlapping communities in directed and undirected networks.

    Conclusions:

    • The proposed MOEA-SA_OV algorithm offers a robust solution for overlapping community detection in attributed networks.
    • The integration of topological and attribute information enhances the accuracy and meaningfulness of detected communities.
    • The algorithm's ability to handle both network types and automatically determine community numbers provides a versatile tool for network analysis.