Similarity-Navigated Conformal Prediction for Graph Neural Networks
Authors: Jianqing Song, Jianguo Huang, Wenyu Jiang, Baoming Zhang, Shuangjie Li, Chongjun Wang
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | 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. |