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A multiobjective evolutionary algorithm based on similarity for community detection from signed social networks.

Chenlong Liu, Jing Liu, Zhongzhou Jiang

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    Summary
    This summary is machine-generated.

    This study introduces MEAs-SN, a novel algorithm for community detection in signed social networks (SNs). MEAs-SN effectively identifies communities in large-scale networks, outperforming existing methods.

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    Area of Science:

    • Network Science
    • Computational Social Science
    • Data Mining

    Background:

    • Social relationships can be modeled as signed social networks (SNs) with positive and negative links.
    • Existing community detection (CD) algorithms are often limited to networks with only positive links.
    • There is a need for CD algorithms capable of handling large-scale SNs.

    Purpose of the Study:

    • To develop a CD algorithm for large-scale signed social networks.
    • To address the limitations of existing algorithms in handling both positive and negative links.
    • To propose a novel multiobjective evolutionary algorithm (MOEA) for SN community detection.

    Main Methods:

    • Extended similarity to signed similarity based on social balance theory.
    • Designed two objective functions for SN community detection as a multiobjective problem.
    • Developed a multiobjective evolutionary algorithm (MEAs-SN) with a combined direct and indirect community representation.

    Main Results:

    • MEAs-SN demonstrates effectiveness and efficiency on networks of 1000, 5000, and 10,000 nodes.
    • The algorithm performs well in various noisy conditions.
    • MEAs-SN outperforms three existing community detection algorithms in comparative experiments.

    Conclusions:

    • MEAs-SN is a robust and effective algorithm for community detection in large-scale signed social networks.
    • The combined representation allows MEAs-SN to benefit from different community structures and detect overlapping communities.
    • The proposed approach advances the field of community detection in complex network analysis.