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..
Graph-Structured Gaussian Processes for Transferable Graph Learning
Authors: Jun Wu, Lisa Ainsworth, Andrew Leakey, Haixun Wang, Jingrui He
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on several transferable graph learning benchmarks demonstrate the efficacy of Graph GP over state-of-the-art Gaussian process baselines. |
| Researcher Affiliation | Collaboration | 1University of Illinois at Urbana-Champaign 2USDA ARS Global Change and Photosynthesis Research Unit 3Instacart |
| Pseudocode | Yes | Algorithm 1 Graph GP |
| Open Source Code | Yes | Code is available at https://github.com/jwu4sml/Graph GP. |
| Open Datasets | Yes | Twitch [44]: It has 6 different domains (...). Agriculture [34, 60]: It has 3 different domains (...). Airports [43]: It has 3 different domains (...). Wikipedia [44]: It has 3 different domains (...). Web KB [41]: It has 3 different domains (...). |
| Dataset Splits | Yes | For Airport, Wikipedia, and Web KB data sets, we randomly select 10% of target nodes for the training set, 10% for the validation set, and 80% for the testing set. For Agriculture and Twitch data sets, we randomly select 1% of target nodes for the training set, 1% for the validation set, and 98% for the testing set. |
| Hardware Specification | Yes | All the experiments are performed on a Windows machine with four 3.80GHz Intel Cores, 64GB RAM, and two NVIDIA Quadro RTX 5000 GPUs. |
| Software Dependencies | No | The paper mentions using "GPy Torch [16]" and "Adam [24]" but does not provide specific version numbers for these software dependencies, which is required for reproducibility. |
| Experiment Setup | Yes | The hyperparameters are optimized using Adam [24] with a learning rate of 0.01 and a total number of training epochs of 500. |