Facilitating Graph Neural Networks with Random Walk on Simplicial Complexes
Authors: Cai Zhou, Xiyuan Wang, Muhan Zhang
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments verify the effectiveness of our random walk-based methods. ... In this section, we present a comprehensive ablation study on Zinc-12k to investigate the effectiveness of our proposed methods. We also verify the performance on graph-level OGB benchmarks. Due to the limited space, experiments on synthetic datasets and more real-world datasets as well as experimental details are presented in Appendix E. |
| Researcher Affiliation | Academia | Cai Zhou Tsinghua University zhouc20@mails.tsinghua.edu.cn Xiyuan Wang Peking University wangxiyuan@pku.edu.cn Muhan Zhang Peking University muhan@pku.edu.cn |
| Pseudocode | No | The paper does not contain pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/zhouc20/Hodge Random Walk. |
| Open Datasets | Yes | Zinc-12k [17] is a popular real-world dataset containing 12k molecules. ... ogbg-molhiv and ogbg-molpcba are from Open Graph Benchmark [26]... PCQM-Contact, Peptides-func and Peptides-struct are from Long-range Graph Benfchmark [19]. |
| Dataset Splits | Yes | We follow the common predefined 10K/1K/1K train/validation/test split. ... we follow the standard dataset splits as the original image classification datasets, i.e., 55K/5K/10K for MNIST and 45K/5K/10K for CIFAR10 of train/validation/test graphs, respectively. |
| Hardware Specification | Yes | For example, the average generation times on Zinc computed by RTX3090 are: RWSE (23s), Edge RWSE (32s), Hodge1Lap (28s). |
| Software Dependencies | No | The paper mentions several deep learning models and frameworks used (e.g., GINE, GAT, GPS), but does not specify programming languages (like Python) or library versions (like PyTorch version numbers) required for replication. |
| Experiment Setup | Yes | To verify that our methods are capable of improving the performance of the base models, all hyperparameters including training configuration and model hyperparameters are set the same as in [38]. For the edge PE/SE, we keep the embedding dimensions the same as the node PE/SE in GPS models. |