Conformal prediction (CP) provides a distribution-free approach to uncertainty quantification with finite-sample guarantees. However, applying CP to graph neural networks (GNNs) remains challenging as the combinatorial nature of graphs often leads to insufficiently certain predictions and indiscriminative embeddings. Existing methods primarily rely on embedding-space proximity for localization, which can be unreliable for graphs and yield inefficient prediction sets. We propose GRAPHLCP, a proximity-based localized CP framework that explicitly incorporates graph topology and inter-node dependencies into localization and weighting. Our approach introduces a feature-aware densification step to mitigate locality bias in sparse graphs, followed by a Personalized PageRank-based kernel computation to model structural proximity. This enables topology-dependent anchor sampling and calibration weighting that captures both local and long-range dependencies. Extensive experiments on several regression and classification datasets demonstrate that GRAPHLCP guarantees marginal coverage with finite samples while efficiently attaining favorable test conditional coverage across various conditioning scenarios.
Stable Localized Conformal Prediction via Transduction
School of Statistics；Data Science, Nankai University, Tianjin, China；School of Mathematics \& Statistics, The University of Melbourne, Melbourne, Australia
Cluster Frequency Conformal Prediction for Local Coverage
Institute of Applied AI Research (AAIR)（应用人工智能研究学院）；Faculty of Computer and Information Science（计算机与信息科学学院）；Ben-Gurion University of the Negev（贝叶尔-加隆大学）；Institute for Interdisciplinary Computational Science (ICS)（跨学科计算科学研究所）
Long-Horizon Traffic Forecasting via Incident-Aware Conformal Spatio-Temporal Transformers
Department of Mechanical and Aerospace Engineering and the Center for Automotive Research, The Ohio State University（机械与航空航天工程系和汽车研究中心，俄亥俄州立大学）；Ohio Department of Transportation（俄亥俄州交通部）；Honda Research Institute USA, Inc.（本田美国研究院）
