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Updated: May 16, 2025

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WAGE: Weight-Sharing Attribute-Missing Graph Autoencoder.

Wenxuan Tu, Sihang Zhou, Xinwang Liu

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |April 4, 2025
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    Summary
    This summary is machine-generated.

    Weight-sharing Attribute-missing Graph autoEncoder (WAGE) effectively reconstructs missing node attributes by integrating attribute and structure information. This approach enhances graph learning by improving data imputation and representation quality.

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    Area of Science:

    • Graph machine learning
    • Data mining
    • Artificial intelligence

    Background:

    • Attribute-missing graph learning is a significant challenge with existing methods lacking noise filtering and effective information integration.
    • Current approaches often isolate node attribute and graph structure encoding, leading to increased parameters and suboptimal use of information.
    • Overly strict distribution assumptions in latent variables can result in biased or less discriminative node representations.

    Purpose of the Study:

    • To propose a novel Weight-sharing Attribute-missing Graph autoEncoder (WAGE) for high-quality missing attribute reconstruction.
    • To enhance the expressive capacity of node representations by fostering information interaction between node attributes and graph structure.
    • To address limitations in existing attribute-missing graph learning techniques.

    Main Methods:

    • Implemented a weight-sharing architecture to entangle attribute and structure embedding, enabling parameter sharing and richer information utilization.
    • Introduced a K-nearest neighbor-based dual non-local learning mechanism for improved data imputation by identifying high-confidence connections and filtering unreliable ones.
    • Incorporated a strategy of masking and recovering adjacency matrices to compel the network to exploit high-order discriminative features for attribute completion.

    Main Results:

    • WAGE demonstrated superior performance in missing attribute reconstruction compared to state-of-the-art methods.
    • The proposed methods effectively improved data imputation quality and the discriminative capacity of node representations.
    • Experiments on six benchmark datasets validated the effectiveness of the WAGE model.

    Conclusions:

    • WAGE offers an effective solution for attribute-missing graph learning by deeply integrating attribute and structure information.
    • The weight-sharing mechanism and dual non-local learning significantly boost model performance.
    • The proposed approach provides a robust and efficient method for enhancing graph representation learning with incomplete attributes.