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..
Sketch-GNN: Scalable Graph Neural Networks with Sublinear Training Complexity
Authors: Mucong Ding, Tahseen Rabbani, Bang An, Evan Wang, Furong Huang
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on large-graph benchmarks demonstrate the scalability and competitive performance of our Sketch-GNNs versus their full-size GNN counterparts. |
| Researcher Affiliation | Academia | Department of Computer Science, University of Maryland EMAIL |
| Pseudocode | Yes | We generalize Sketch-GNN to more GNN models in Appendix D and the pseudo-code which outlines the complete workflow of Sketch-GNN can be find in Appendix E. |
| Open Source Code | Yes | Our code will be made publicly available at https://github.com/SketchGNN/SketchGNN. |
| Open Datasets | Yes | We test on two small graph benchmarks including Cora, Citeseer and several large graph benchmarks including ogbn-arxiv (169K nodes, 1.2M edges), Reddit (233K nodes, 11.6M edges), and ogbn-products (2.4M nodes, 61.9M edges) from [20, 45]. |
| Dataset Splits | Yes | Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] See Appendix H. |
| Hardware Specification | Yes | All experiments are conducted on an AWS EC2 instance with 192GB RAM and 8 NVIDIA A100 GPUs. |
| Software Dependencies | No | Our implementation is based on PyTorch Geometric (PyG) [20] and DGL [49]. The software dependencies can be found in our Github repo. |
| Experiment Setup | Yes | Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] See Appendix H. |