Conformal Prediction: A Theoretical Note and Benchmarking Transductive Node Classification in Graphs
This work provides incremental improvements for researchers in graph machine learning by clarifying and scaling conformal prediction methods.
The paper addresses conflicting design choices in conformal prediction for graph uncertainty quantification by analyzing tradeoffs in existing methods and introducing scalable techniques for large-scale graph datasets, achieving performance without sacrifice as justified by theoretical and empirical results.
Conformal prediction has become increasingly popular for quantifying the uncertainty associated with machine learning models. Recent work in graph uncertainty quantification has built upon this approach for conformal graph prediction. The nascent nature of these explorations has led to conflicting choices for implementations, baselines, and method evaluation. In this work, we analyze the design choices made in the literature and discuss the tradeoffs associated with existing methods. Building on the existing implementations, we introduce techniques to scale existing methods to large-scale graph datasets without sacrificing performance. Our theoretical and empirical results justify our recommendations for future scholarship in graph conformal prediction.