FSD-CAP: Fractional Subgraph Diffusion with Class-Aware Propagation for Graph Feature Imputation
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866915757425688576 |
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| author | Qiao, Xin Sun, Shijie Dong, Anqi Hua, Cong Zhao, Xia Zhang, Longfei Zhu, Guangming Zhang, Liang |
| author_facet | Qiao, Xin Sun, Shijie Dong, Anqi Hua, Cong Zhao, Xia Zhang, Longfei Zhu, Guangming Zhang, Liang |
| contents | Imputing missing node features in graphs is challenging, particularly under high missing rates. Existing methods based on latent representations or global diffusion often fail to produce reliable estimates, and may propagate errors across the graph. We propose FSD-CAP, a two-stage framework designed to improve imputation quality under extreme sparsity. In the first stage, a graph-distance-guided subgraph expansion localizes the diffusion process. A fractional diffusion operator adjusts propagation sharpness based on local structure. In the second stage, imputed features are refined using class-aware propagation, which incorporates pseudo-labels and neighborhood entropy to promote consistency. We evaluated FSD-CAP on multiple datasets. With $99.5\%$ of features missing across five benchmark datasets, FSD-CAP achieves average accuracies of $80.06\%$ (structural) and $81.01\%$ (uniform) in node classification, close to the $81.31\%$ achieved by a standard GCN with full features. For link prediction under the same setting, it reaches AUC scores of $91.65\%$ (structural) and $92.41\%$ (uniform), compared to $95.06\%$ for the fully observed case. Furthermore, FSD-CAP demonstrates superior performance on both large-scale and heterophily datasets when compared to other models. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_18938 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | FSD-CAP: Fractional Subgraph Diffusion with Class-Aware Propagation for Graph Feature Imputation Qiao, Xin Sun, Shijie Dong, Anqi Hua, Cong Zhao, Xia Zhang, Longfei Zhu, Guangming Zhang, Liang Machine Learning Social and Information Networks 68T07, 68Q32, 90C26 Imputing missing node features in graphs is challenging, particularly under high missing rates. Existing methods based on latent representations or global diffusion often fail to produce reliable estimates, and may propagate errors across the graph. We propose FSD-CAP, a two-stage framework designed to improve imputation quality under extreme sparsity. In the first stage, a graph-distance-guided subgraph expansion localizes the diffusion process. A fractional diffusion operator adjusts propagation sharpness based on local structure. In the second stage, imputed features are refined using class-aware propagation, which incorporates pseudo-labels and neighborhood entropy to promote consistency. We evaluated FSD-CAP on multiple datasets. With $99.5\%$ of features missing across five benchmark datasets, FSD-CAP achieves average accuracies of $80.06\%$ (structural) and $81.01\%$ (uniform) in node classification, close to the $81.31\%$ achieved by a standard GCN with full features. For link prediction under the same setting, it reaches AUC scores of $91.65\%$ (structural) and $92.41\%$ (uniform), compared to $95.06\%$ for the fully observed case. Furthermore, FSD-CAP demonstrates superior performance on both large-scale and heterophily datasets when compared to other models. |
| title | FSD-CAP: Fractional Subgraph Diffusion with Class-Aware Propagation for Graph Feature Imputation |
| topic | Machine Learning Social and Information Networks 68T07, 68Q32, 90C26 |
| url | https://arxiv.org/abs/2601.18938 |