Graph Sparsification for Enhanced Conformal Prediction in Graph Neural Networks
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866910674443042816 |
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| author | He, Yuntian Maneriker, Pranav Srinivasan, Anutam Vadlamani, Aditya T. Parthasarathy, Srinivasan |
| author_facet | He, Yuntian Maneriker, Pranav Srinivasan, Anutam Vadlamani, Aditya T. Parthasarathy, Srinivasan |
| contents | Conformal Prediction is a robust framework that ensures reliable coverage across machine learning tasks. Although recent studies have applied conformal prediction to graph neural networks, they have largely emphasized post-hoc prediction set generation. Improving conformal prediction during the training stage remains unaddressed. In this work, we tackle this challenge from a denoising perspective by introducing SparGCP, which incorporates graph sparsification and a conformal prediction-specific objective into GNN training. SparGCP employs a parameterized graph sparsification module to filter out task-irrelevant edges, thereby improving conformal prediction efficiency. Extensive experiments on real-world graph datasets demonstrate that SparGCP outperforms existing methods, reducing prediction set sizes by an average of 32\% and scaling seamlessly to large networks on commodity GPUs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_21618 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Graph Sparsification for Enhanced Conformal Prediction in Graph Neural Networks He, Yuntian Maneriker, Pranav Srinivasan, Anutam Vadlamani, Aditya T. Parthasarathy, Srinivasan Machine Learning Conformal Prediction is a robust framework that ensures reliable coverage across machine learning tasks. Although recent studies have applied conformal prediction to graph neural networks, they have largely emphasized post-hoc prediction set generation. Improving conformal prediction during the training stage remains unaddressed. In this work, we tackle this challenge from a denoising perspective by introducing SparGCP, which incorporates graph sparsification and a conformal prediction-specific objective into GNN training. SparGCP employs a parameterized graph sparsification module to filter out task-irrelevant edges, thereby improving conformal prediction efficiency. Extensive experiments on real-world graph datasets demonstrate that SparGCP outperforms existing methods, reducing prediction set sizes by an average of 32\% and scaling seamlessly to large networks on commodity GPUs. |
| title | Graph Sparsification for Enhanced Conformal Prediction in Graph Neural Networks |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2410.21618 |