Graph Sparsification for Enhanced Conformal Prediction in Graph Neural Networks

• URL: http://arxiv.org/abs/2410.21618v1
• Date: Mon, 28 Oct 2024 23:53:51 GMT
• Title: Graph Sparsification for Enhanced Conformal Prediction in Graph Neural Networks
• Authors: Yuntian He, Pranav Maneriker, Anutam Srinivasan, Aditya T. Vadlamani, Srinivasan Parthasarathy,
• Abstract summary: Conformal Prediction is a robust framework that ensures reliable coverage across machine learning tasks. SparGCP incorporates graph sparsification and a conformal prediction-specific objective into GNN training. Experiments on real-world graph datasets demonstrate that SparGCP outperforms existing methods.
• Score: 5.896352342095999
• License:
• Abstract: 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.

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