Détail du document
Identifiant
oai:arXiv.org:2405.14303
Sujet
Computer Science - Machine Learnin...Auteur
Song, Jianqing Huang, Jianguo Jiang, Wenyu Zhang, Baoming Li, Shuangjie Wang, ChongjunCatégorie
Computer Science
Année
2024
Date de référencement
29/05/2024
Mots clés
node label nodes snaps conformal setsRésumé
Graph Neural Networks have achieved remarkable accuracy in semi-supervised node classification tasks.
However, these results lack reliable uncertainty estimates.
Conformal prediction methods provide a theoretical guarantee for node classification tasks, ensuring that the conformal prediction set contains the ground-truth label with a desired probability (e.g., 95%).
In this paper, we empirically show that for each node, aggregating the non-conformity scores of nodes with the same label can improve the efficiency of conformal prediction sets.
This observation motivates us to propose a novel algorithm named Similarity-Navigated Adaptive Prediction Sets (SNAPS), which aggregates the non-conformity scores based on feature similarity and structural neighborhood.
The key idea behind SNAPS is that nodes with high feature similarity or direct connections tend to have the same label.
By incorporating adaptive similar nodes information, SNAPS can generate compact prediction sets and increase the singleton hit ratio (correct prediction sets of size one).
Moreover, we theoretically provide a finite-sample coverage guarantee of SNAPS.
Extensive experiments demonstrate the superiority of SNAPS, improving the efficiency of prediction sets and singleton hit ratio while maintaining valid coverage.
Song, Jianqing,Huang, Jianguo,Jiang, Wenyu,Zhang, Baoming,Li, Shuangjie,Wang, Chongjun, 2024, Similarity-Navigated Conformal Prediction for Graph Neural Networks
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