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.
@article{arxiv.2410.21618,
title = {Graph Sparsification for Enhanced Conformal Prediction in Graph Neural Networks},
author = {Yuntian He and Pranav Maneriker and Anutam Srinivasan and Aditya T. Vadlamani and Srinivasan Parthasarathy},
journal= {arXiv preprint arXiv:2410.21618},
year = {2024}
}