Geometric Imbalance in Semi-Supervised Node Classification
Fuente:
arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| Soggetti: | |
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| _version_ | 1866908902958825472 |
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| author | Yan, Liang Zhang, Shengzhong Li, Bisheng Yang, Menglin Yang, Chen Zhou, Min Ding, Weiyang Xie, Yutong Huang, Zengfeng |
| author_facet | Yan, Liang Zhang, Shengzhong Li, Bisheng Yang, Menglin Yang, Chen Zhou, Min Ding, Weiyang Xie, Yutong Huang, Zengfeng |
| contents | Class imbalance in graph data presents a significant challenge for effective node classification, particularly in semi-supervised scenarios. In this work, we formally introduce the concept of geometric imbalance, which captures how message passing on class-imbalanced graphs leads to geometric ambiguity among minority-class nodes in the riemannian manifold embedding space. We provide a rigorous theoretical analysis of geometric imbalance on the riemannian manifold and propose a unified framework that explicitly mitigates it through pseudo-label alignment, node reordering, and ambiguity filtering. Extensive experiments on diverse benchmarks show that our approach consistently outperforms existing methods, especially under severe class imbalance. Our findings offer new theoretical insights and practical tools for robust semi-supervised node classification. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_10371 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Geometric Imbalance in Semi-Supervised Node Classification Yan, Liang Zhang, Shengzhong Li, Bisheng Yang, Menglin Yang, Chen Zhou, Min Ding, Weiyang Xie, Yutong Huang, Zengfeng Machine Learning Class imbalance in graph data presents a significant challenge for effective node classification, particularly in semi-supervised scenarios. In this work, we formally introduce the concept of geometric imbalance, which captures how message passing on class-imbalanced graphs leads to geometric ambiguity among minority-class nodes in the riemannian manifold embedding space. We provide a rigorous theoretical analysis of geometric imbalance on the riemannian manifold and propose a unified framework that explicitly mitigates it through pseudo-label alignment, node reordering, and ambiguity filtering. Extensive experiments on diverse benchmarks show that our approach consistently outperforms existing methods, especially under severe class imbalance. Our findings offer new theoretical insights and practical tools for robust semi-supervised node classification. |
| title | Geometric Imbalance in Semi-Supervised Node Classification |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2303.10371 |