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    Area of Science:

    • Computer Science
    • Data Science
    • Graph Theory

    Background:

    • Analyzing structural differences in graphs is crucial for understanding dynamic patterns and graph evolution.
    • Existing representation learning methods for graphs often lack intuitive ways to study structural semantics.
    • There is a need for methods that can effectively encode and analyze the semantic nuances of structural differences across multiple graphs.

    Purpose of the Study:

    • To propose a novel representation-and-analysis scheme for structural differences among graphs.
    • To develop a deep-learning-based embedding technique that preserves the semantics of structural differences.
    • To create a visual analytics system for intuitive comparative study of learned graph features.

    Main Methods:

    • A deep-learning-based embedding technique was developed to encode multiple graphs.
    • A web-based visual analytics system was designed and implemented for comparative analysis.
    • The approach supports semantics-aware construction, quantification, and investigation of latent relations.

    Main Results:

    • The proposed method effectively encodes multiple graphs while preserving semantic differences.
    • The visual analytics system facilitates intuitive comparative study of learned graph embeddings.
    • Case studies on three datasets demonstrated the usability and effectiveness of the approach.

    Conclusions:

    • The developed scheme offers an intuitive way to study structural semantics of graphs.
    • The combination of deep learning embeddings and visual analytics enhances the analysis of graph structural differences.
    • This approach provides a powerful tool for investigating latent relations within and across graphs.