Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Similarity-Navigated Conformal Prediction for Graph Neural Networks
Authors: Jianqing Song, Jianguo Huang, Wenyu Jiang, Baoming Zhang, Shuangjie Li, Chongjun Wang
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate the superiority of SNAPS, improving the efficiency of prediction sets and singleton hit ratio while maintaining valid coverage. |
| Researcher Affiliation | Academia | 1State Key Laboratory of Novel Software Technology, Nanjing University 2Department of Statistics and Data Science, Southern University of Science and Technology 3College of Computing and Data Science, Nanyang Technological University |
| Pseudocode | Yes | The pseudo-code for SNAPS is presented in Algorithm 1. |
| Open Source Code | Yes | Code is available at https://github.com/janqsong/SNAPS. |
| Open Datasets | Yes | We conduct thorough empirical evaluations on 10 datasets, including both small datasets and large-scale datasets, e.g., OGBN Products (Bhatia et al., 2016). |
| Dataset Splits | Yes | Vlabel is then randomly split into Vtrain/Vvalid/Vcalib with a fixed size, the training/validation/calibration node set, correspondingly. |
| Hardware Specification | Yes | Each experiment is done with a single NVIDIA V100 32GB GPU. |
| Software Dependencies | No | The paper mentions using 'PyTorch Geometric' and 'torchvision repository' but does not specify software versions for these or other dependencies like Python or PyTorch itself. |
| Experiment Setup | Yes | For SNAPS, we choose "lambda" and "mu" in increments of 0.05 within the range 0 to 1, and ensure that "lambda" + "mu" <= 1. |