Unsupervised Neighborhood Propagation Kernel Layers for Semi-supervised Node Classification
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866917639542013952 |
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| author | Achten, Sonny Tonin, Francesco Patrinos, Panagiotis Suykens, Johan A. K. |
| author_facet | Achten, Sonny Tonin, Francesco Patrinos, Panagiotis Suykens, Johan A. K. |
| contents | We present a deep Graph Convolutional Kernel Machine (GCKM) for semi-supervised node classification in graphs. The method is built of two main types of blocks: (i) We introduce unsupervised kernel machine layers propagating the node features in a one-hop neighborhood, using implicit node feature mappings. (ii) We specify a semi-supervised classification kernel machine through the lens of the Fenchel-Young inequality. We derive an effective initialization scheme and efficient end-to-end training algorithm in the dual variables for the full architecture. The main idea underlying GCKM is that, because of the unsupervised core, the final model can achieve higher performance in semi-supervised node classification when few labels are available for training. Experimental results demonstrate the effectiveness of the proposed framework. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2301_13764 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Unsupervised Neighborhood Propagation Kernel Layers for Semi-supervised Node Classification Achten, Sonny Tonin, Francesco Patrinos, Panagiotis Suykens, Johan A. K. Machine Learning We present a deep Graph Convolutional Kernel Machine (GCKM) for semi-supervised node classification in graphs. The method is built of two main types of blocks: (i) We introduce unsupervised kernel machine layers propagating the node features in a one-hop neighborhood, using implicit node feature mappings. (ii) We specify a semi-supervised classification kernel machine through the lens of the Fenchel-Young inequality. We derive an effective initialization scheme and efficient end-to-end training algorithm in the dual variables for the full architecture. The main idea underlying GCKM is that, because of the unsupervised core, the final model can achieve higher performance in semi-supervised node classification when few labels are available for training. Experimental results demonstrate the effectiveness of the proposed framework. |
| title | Unsupervised Neighborhood Propagation Kernel Layers for Semi-supervised Node Classification |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2301.13764 |