Safe multi-view graph convolutional network for semi-supervised classification

Dan Xiang

doi:10.1016/j.neucom.2025.132570

Safe multi-view graph convolutional network for semi-supervised classification

2026-01-02

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DOI：10.1016/j.neucom.2025.132570

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Graph Convolutional Network (GCN) is widely used in multi-view semi-supervised learning for its ability to capture structural and relational information. However, as the number of views increases, existing GCN-based methods often suffer from noise and inconsistencies while fusing multi-view information, leading to performance degradation. To address this issue, we propose a novel GCN-based method called Safe Multi-view Semi-supervised GCN (SMSGCN), which mitigates the risk of performance degradation caused by an increase. Our method integrates a reconstruction objective with Laplacian embedding and a safe mechanism into a unified GCN-based framework. Specifically, we use a reconstruction error based on Laplacian embedding to capture cross-view complementarity, and a safe module that dynamically adjusts safe coefficients to emphasize informative views and suppress noisy newly increased views, preventing performance degradation as the number of views increases. As a result, it can adaptively select informative views while suppressing noisy ones, thereby ensuring stable performance. In addition, we define safety from the perspective of empirical classification risk and theoretically prove that our method can achieve empirically safe multi-view semi-supervised classification. Extensive experiments conducted on multiple public benchmark datasets validate the effectiveness, robustness, and superiority of the proposed method in achieving safe multi-view semi-supervised classification.

Safe multi-view graph convolutional network for semi-supervised classification

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