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Updated: Aug 3, 2025

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Semi-Supervised Mixture Learning for Graph Neural Networks With Neighbor Dependence.

Kai Liu, Hongbo Liu, Tao Wang

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    Graph coneighbor neural networks (GCoNN) enhance semi-supervised learning by improving attribute learning and structure capture. This novel framework achieves state-of-the-art performance in node classification, even with limited data.

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    Area of Science:

    • Machine Learning
    • Graph Neural Networks
    • Artificial Intelligence

    Background:

    • Graph Neural Networks (GNNs) are effective for semi-supervised learning (SSL) but struggle with incomplete attributes and structure capture, especially in label-scarce or attribute-missing scenarios.
    • Existing GNN models face challenges in distinguishing between node attributes and graph structure, limiting their performance on complex datasets.
    • The data-driven nature of GNNs necessitates novel approaches to overcome inherent limitations in attribute learning and structural representation.

    Purpose of the Study:

    • To introduce a novel framework, Graph Coneighbor Neural Network (GCoNN), designed to address the limitations of existing GNNs in node classification.
    • To enhance attribute learning and graph structure capture within a semi-supervised learning context.
    • To improve the integration of node attributes and structural information for more robust node classification.

    Main Methods:

    • Propose a two-module framework: GCoNNΓ for attribute learning and GCoNNΓ for neighbor dependence learning using pseudo-labels.
    • Employ an iterative retraining process where GCoNNΓ is refined based on feedback from GCoNNΓ to better integrate attributes and structure.
    • Analyze the iterative convergence using a generalized expectation-maximization (GEM) framework and amortized variational inference to optimize the evidence lower bound (ELBO).

    Main Results:

    • GCoNN demonstrates state-of-the-art performance in node classification tasks, outperforming existing methods.
    • The framework effectively handles label-scarce and attribute-missing data by improving attribute learning and structure capture.
    • Application to brain functional networks reveals physiologically plausible response features related to language and visual functions.

    Conclusions:

    • GCoNN offers a significant advancement in graph neural network architectures for semi-supervised learning and node classification.
    • The proposed iterative approach successfully integrates node attributes and graph structure, leading to superior performance.
    • The framework's applicability to real-world data, such as brain networks, highlights its potential for diverse scientific discoveries.