Graph Adversarial Diffusion Convolution
Authors: Songtao Liu, Jinghui Chen, Tianfan Fu, Lu Lin, Marinka Zitnik, Dinghao Wu
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate the effectiveness of GADC across various datasets. |
| Researcher Affiliation | Academia | 1The Pennsylvania State University 2Rensselaer Polytechnic Institute 3Harvard University. |
| Pseudocode | Yes | Algorithm 1 Graph Adversarial Diffusion Convolution |
| Open Source Code | Yes | Code is available at https://github.com/ Songtao Liu0823/GADC. |
| Open Datasets | Yes | For our experiments, we use three small-scale graph datasets: Cora, Citeseer, and Pubmed, and three large-scale graph datasets: Coauthor-CS, Coauthor-Phy (Shchur et al., 2018), and ogbn-products (Hu et al., 2020). |
| Dataset Splits | Yes | For the Coauthor datasets, we split the nodes into 60% for training, 20% for validation, and 20% for testing. |
| Hardware Specification | Yes | All the experiments in this work are conducted on a single NVIDIA Tesla A100 with 80GB memory size. |
| Software Dependencies | Yes | The software that we use for experiments are Python 3.6.8, pytorch 1.9.0, pytorch-scatter 2.0.9, pytorch-sparse 0.6.12, pyg 2.0.3, ogb 1.3.4, numpy 1.19.5, torchvision 0.10.0, and CUDA 11.1. |
| Experiment Setup | Yes | We provide details about the hyparatemeters of GADC in Table 8, 9, 10, 11, and 12. |