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    Area of Science:

    • Machine Learning
    • Data Mining
    • Graph Theory

    Background:

    • Multi-View Graph Clustering (MVGC) methods are widely used but often overlook crucial cross-view interactions.
    • Existing MVGC approaches primarily focus on fusing graph information, limiting performance potential.

    Purpose of the Study:

    • To enhance MVGC performance by developing a method that leverages cross-view interactions and graph signal processing.
    • To introduce a novel perspective on graph clustering using graph signal processing and local preferences.

    Main Methods:

    • Designed a cross-view graph enhancement module to explore topological structures and interactions.
    • Adapted the high-order Graph Trend Filter from graph signal processing to analyze graph smoothness.
    • Proposed the Enhanced Graph Trend Filter Clustering (EGTFC) method with a corresponding optimization algorithm.

    Main Results:

    • The proposed EGTFC method effectively addresses limitations in existing MVGC techniques.
    • Experimental results on twelve benchmark datasets validate the superiority of EGTFC.
    • EGTFC demonstrated significant performance improvements over thirteen state-of-the-art MVGC methods.

    Conclusions:

    • The integration of cross-view interactions and graph signal processing offers a promising direction for MVGC.
    • The EGTFC method provides a robust and effective solution for multi-view graph clustering tasks.
    • The study highlights the importance of considering graph smoothness and local preferences in clustering.