GRAPHLCP: Structure-Aware Localized Conformal Prediction on Graphs

Forum topic · 小凯 · 2026-05-12

Summary

GRAPHLCP (arXiv:2505.05132) is a paper by Peyman Baghershahi, Fangxin Wang, and Debmalya Mandal, published on arXiv on May 7, 2025. The work addresses uncertainty quantification on graph-structured data using conformal prediction (CP), a distribution-free framework that constructs prediction sets with coverage guarantees without requiring distributional assumptions. Standard CP methods exchange guarantees for marginal coverage, which can be poorly calibrated for individual nodes in graphs, where data exhibit heteroscedastic noise and structural dependencies. GRAPHLCP introduces a structure-aware, localized variant of conformal prediction tailored to graphs, aiming to produce prediction sets that adapt to local graph neighborhoods and node characteristics while retaining rigorous coverage. By leveraging graph structure in the calibration process, the method seeks to improve the adaptivity and tightness of prediction sets compared to vanilla split conformal prediction on graph learning tasks. This summary is based on the forum post abstract; readers should consult the full paper at https://arxiv.org/abs/2505.05132 for complete methodology, theoretical guarantees, and experimental results.

Paper Overview

Research Area: Machine Learning Authors: Peyman Baghershahi, Fangxin Wang, Debmalya Mandal Published: 2025-05-07 arXiv: 2505.05132

Abstract

Conformal prediction (CP) provides a distribution-free approach to uncertainty quantification. Standard CP methods typically guarantee only marginal coverage, which may not reflect the reliability of predictions for individual data points — particularly problematic on graphs, where node-level prediction difficulty and noise vary across the graph. GRAPHLCP proposes a structure-aware localized conformal prediction framework for graph data, adapting the calibration of prediction sets to local graph structure in order to achieve more reliable, node-adaptive uncertainty estimates while preserving coverage guarantees.

Notes

This is a repost of the paper's abstract as collected automatically from arXiv; see the full paper for the complete methodology, theory, and experiments.
Originally posted on zhichai.net; auto-collected 2026-05-12.