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    Area of Science:

    • Machine Learning
    • Graph Theory
    • Statistical Modeling

    Background:

    • Traditional graph clustering faces scalability limitations.
    • Existing multi-view bipartite graph clustering (MVBGC) variants often result in complex models.
    • These models struggle to reveal inherent variable relationships.

    Purpose of the Study:

    • To introduce probabilistic graphical models for MVBGC.
    • To reformulate MVBGC as a maximum likelihood estimation (MLE) problem.
    • To develop an interpretable, concise, and flexible MVBGC framework.

    Main Methods:

    • Introduced probabilistic graphical models for MVBGC.
    • Reformulated the task as a maximum likelihood estimation (MLE) problem.
    • Derived the Generalized Probabilistic Graphical Modeling (GProM) framework by incorporating clustering constraints and minimizing noise.

    Main Results:

    • The GProM framework effectively models underlying probabilistic correlations.
    • Minimizing total noise approximates the lower bound of MLE for multi-view data.
    • Extensive experiments verified the framework's effectiveness.

    Conclusions:

    • The GProM framework provides an interpretable, concise, and flexible solution for MVBGC.
    • Statistical analysis offers valuable insights into distribution assumptions for model design.
    • This approach advances unsupervised learning for complex graph structures.