AI RESEARCH
GRAPHLCP: Structure-Aware Localized Conformal Prediction on Graphs
arXiv CS.LG
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ArXi:2605.08074v1 Announce Type: new 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.