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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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DisenSemi: Semi-Supervised Graph Classification via Disentangled Representation Learning.

Yifan Wang, Xiao Luo, Chong Chen

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    Summary
    This summary is machine-generated.

    This study introduces DisenSemi, a new framework for semi-supervised graph classification. It effectively transfers knowledge by disentangling representations, improving performance when labeled data is scarce.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Graph classification is vital for multimedia applications but suffers from limited labeled data.
    • Semi-supervised learning leverages both labeled and unlabeled data to address data scarcity.

    Purpose of the Study:

    • To develop a novel framework, DisenSemi, for semi-supervised graph classification.
    • To enable effective knowledge transfer by learning disentangled representations.

    Main Methods:

    • A disentangled graph encoder generates factorwise representations for supervised and unsupervised models.
    • Models are trained using supervised objectives and mutual information (MI)-based constraints.
    • MI-based disentangled consistency regularization ensures meaningful knowledge transfer.

    Main Results:

    • DisenSemi demonstrates effectiveness across various public datasets.
    • The framework successfully learns disentangled representations for improved graph classification.
    • The proposed method shows superior performance compared to existing approaches.

    Conclusions:

    • DisenSemi offers an effective solution for semi-supervised graph classification with limited labeled data.
    • Disentangled representation learning is crucial for targeted knowledge transfer in graph classification.
    • The framework provides a robust approach for multimedia data analysis using graphs.