AGMixup: Adaptive Graph Mixup for Semi-supervised Node Classification
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909424548839424 |
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| author | Lu, Weigang Guan, Ziyu Zhao, Wei Yang, Yaming Zhan, Yibing Lu, Yiheng Tao, Dapeng |
| author_facet | Lu, Weigang Guan, Ziyu Zhao, Wei Yang, Yaming Zhan, Yibing Lu, Yiheng Tao, Dapeng |
| contents | Mixup is a data augmentation technique that enhances model generalization by interpolating between data points using a mixing ratio $λ$ in the image domain. Recently, the concept of mixup has been adapted to the graph domain through node-centric interpolations. However, these approaches often fail to address the complexity of interconnected relationships, potentially damaging the graph's natural topology and undermining node interactions. Furthermore, current graph mixup methods employ a one-size-fits-all strategy with a randomly sampled $λ$ for all mixup pairs, ignoring the diverse needs of different pairs. This paper proposes an Adaptive Graph Mixup (AGMixup) framework for semi-supervised node classification. AGMixup introduces a subgraph-centric approach, which treats each subgraph similarly to how images are handled in Euclidean domains, thus facilitating a more natural integration of mixup into graph-based learning. We also propose an adaptive mechanism to tune the mixing ratio $λ$ for diverse mixup pairs, guided by the contextual similarity and uncertainty of the involved subgraphs. Extensive experiments across seven datasets on semi-supervised node classification benchmarks demonstrate AGMixup's superiority over state-of-the-art graph mixup methods. Source codes are available at \url{https://github.com/WeigangLu/AGMixup}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_08144 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AGMixup: Adaptive Graph Mixup for Semi-supervised Node Classification Lu, Weigang Guan, Ziyu Zhao, Wei Yang, Yaming Zhan, Yibing Lu, Yiheng Tao, Dapeng Machine Learning Artificial Intelligence Mixup is a data augmentation technique that enhances model generalization by interpolating between data points using a mixing ratio $λ$ in the image domain. Recently, the concept of mixup has been adapted to the graph domain through node-centric interpolations. However, these approaches often fail to address the complexity of interconnected relationships, potentially damaging the graph's natural topology and undermining node interactions. Furthermore, current graph mixup methods employ a one-size-fits-all strategy with a randomly sampled $λ$ for all mixup pairs, ignoring the diverse needs of different pairs. This paper proposes an Adaptive Graph Mixup (AGMixup) framework for semi-supervised node classification. AGMixup introduces a subgraph-centric approach, which treats each subgraph similarly to how images are handled in Euclidean domains, thus facilitating a more natural integration of mixup into graph-based learning. We also propose an adaptive mechanism to tune the mixing ratio $λ$ for diverse mixup pairs, guided by the contextual similarity and uncertainty of the involved subgraphs. Extensive experiments across seven datasets on semi-supervised node classification benchmarks demonstrate AGMixup's superiority over state-of-the-art graph mixup methods. Source codes are available at \url{https://github.com/WeigangLu/AGMixup}. |
| title | AGMixup: Adaptive Graph Mixup for Semi-supervised Node Classification |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2412.08144 |