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..
A Unified Lottery Ticket Hypothesis for Graph Neural Networks
Authors: Tianlong Chen, Yongduo Sui, Xuxi Chen, Aston Zhang, Zhangyang Wang
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our proposal has been experimentally verified across various GNN architectures and diverse tasks, on both small-scale graph datasets (Cora, Citeseer and Pub Med), and large-scale datasets from the challenging Open Graph Benchmark (OGB). |
| Researcher Affiliation | Collaboration | 1Department of Electrical and Computer Engineering, University of Texas at Austin 2University of Science and Technology of China 3AWS Deep Learning. |
| Pseudocode | Yes | Algorithm 1 Unified GNN Sparsification (UGS) |
| Open Source Code | Yes | Codes are at https://github. com/VITA-Group/Unified-LTH-GNN. |
| Open Datasets | Yes | Datasets We use popular semi-supervised graph datasets: Cora, Citeseer and Pub Med (Kipf & Welling, 2016), for both node classification and link prediction tasks. For experiments on large-scale graphs, we use the Open Graph Benchmark (OGB) (Hu et al., 2020), such as Ogbn-Ar Xiv, Ogbn Proteins, and Ogbl-Collab. |
| Dataset Splits | Yes | Other details such as the datasets trainval-test splits are included in Appendix A1. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'NetworkX' but does not provide specific version numbers for software dependencies or frameworks used for the experiments. |
| Experiment Setup | Yes | More detailed configurations such as learning rate, training iterations, and hyperparameters in UGS, are referred to Appendix A1. |