Sketch-GNN: Scalable Graph Neural Networks with Sublinear Training Complexity
Authors: Mucong Ding, Tahseen Rabbani, Bang An, Evan Wang, Furong Huang
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | 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 {mcding, trabbani, bangan, furongh}@cs.umd.edu |
| 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. |