Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Graph Adversarial Diffusion Convolution
Authors: Songtao Liu, Jinghui Chen, Tianfan Fu, Lu Lin, Marinka Zitnik, Dinghao Wu
ICML 2024 | Venue PDF | LLM Run Details | Input Tokens: 27,957 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,531 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |