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Updated: Jul 2, 2025

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Motif-Based Contrastive Learning for Community Detection.

Xunxun Wu, Chang-Dong Wang, Jia-Qi Lin

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    |February 26, 2024
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    Summary
    This summary is machine-generated.

    This study introduces MotifCC, a novel deep learning framework for community detection in complex networks. MotifCC effectively integrates higher-order network structures using motifs and contrastive learning, improving accuracy over existing methods.

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    Area of Science:

    • Complex Network Analysis
    • Machine Learning
    • Data Mining

    Background:

    • Community detection is crucial for understanding complex networks.
    • Existing methods often overlook higher-order connectivity patterns and nonlinear relationships.
    • Motifs are increasingly recognized for their role in network analysis.

    Purpose of the Study:

    • To propose a novel deep learning framework, MotifCC, for enhanced community detection.
    • To effectively fuse higher-order and lower-order structural information in networks.
    • To address limitations of shallow methods in capturing intricate node relationships.

    Main Methods:

    • Constructing a higher-order network based on network motifs.
    • Creating subnetworks by removing isolated nodes to mitigate fragmentation.
    • Applying contrastive learning to integrate node, edge, and structural information.
    • Utilizing label propagation on subnetwork community structures to assign community labels.

    Main Results:

    • MotifCC successfully integrates diverse network information (nodes, edges, higher/lower order structures).
    • The framework maximizes similarity for corresponding nodes while distinguishing different nodes and communities.
    • Extensive experiments on real-world datasets demonstrate the effectiveness of MotifCC.

    Conclusions:

    • MotifCC offers a significant advancement in community detection by leveraging higher-order network structures.
    • The proposed deep learning framework provides a more comprehensive analysis of complex networks.
    • MotifCC demonstrates superior performance compared to existing community detection methods.