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A Systematic Survey on Deep Generative Models for Graph Generation.

Xiaojie Guo, Liang Zhao

    IEEE Transactions on Pattern Analysis and Machine Intelligence
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    Summary
    This summary is machine-generated.

    Deep generative models enhance graph generation by learning complex data distributions. This review overviews deep graph generation techniques, their applications, and future research directions.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Data Science

    Background:

    • Graphs are crucial for representing relationships in diverse real-world scenarios.
    • Traditional graph generation methods are often hand-crafted and limited in modeling graph properties.
    • Deep generative models represent a significant advancement in generating high-fidelity graphs.

    Purpose of the Study:

    • To provide a comprehensive overview of deep generative models for graph generation.
    • To categorize and analyze existing unconditional and conditional graph generation models.
    • To highlight applications and future research directions in deep graph generation.

    Main Methods:

    • Literature review of deep generative models for graph generation.
    • Taxonomies for unconditional and conditional graph generation models.
    • Analysis and comparison of existing works and evaluation metrics.

    Main Results:

    • Detailed overview of deep generative models for graph generation.
    • Classification and analysis of unconditional and conditional models.
    • Summary of evaluation metrics and emerging applications.

    Conclusions:

    • Deep generative models significantly improve graph generation fidelity and enable new applications.
    • The field offers promising future research directions in both model development and application.
    • This review serves as a foundational resource for researchers in deep graph generation.