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.
G-Mixup: Graph Data Augmentation for Graph Classification
Authors: Xiaotian Han, Zhimeng Jiang, Ninghao Liu, Xia Hu
ICML 2022 | Venue PDF | LLM Run Details | Input Tokens: 29,609 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,404 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 show that G-Mixup substantially improves the generalization and robustness of GNNs. |
| Researcher Affiliation | Academia | 1Department of Computer Science&Engineering, Texas A&M University 2Department of Computer Science, University of Georgia 3Department of Computer Science, Rice University. |
| Pseudocode | Yes | Algorithm 1 Graphon Estimation ... Algorithm 2 G-Mixup ... Algorithm 3 G-Mixup (batch) |
| Open Source Code | Yes | Our code is available at https://github.com/ahxt/g-mixup. |
| Open Datasets | Yes | We evaluate the performance of G-Mixup ... with various datasets and GNN backbones in Section 5.3... Dataset IMDB-B IMDB-M REDD-B REDD-M5 REDD-M12... We experiment on molecular property prediction task (Hu et al., 2020), including ogbg-molhiv, ogbg-molbace, ogbgmolbbbp. |
| Dataset Splits | Yes | We split the dataset into train/val/test data by 7 : 1 : 2. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper mentions specific algorithms and models like 'Adam optimizer(Kingma & Ba, 2015)', 'GCN (Kipf & Welling, 2017)', 'GIN (Xu et al., 2018)', and refers to implementations like 'https://github.com/pyg-team/pytorch_geometric/blob/1.7.2/examples/gcn2_ppi.py'. However, it does not provide a list of ancillary software dependencies with specific version numbers (e.g., Python version, PyTorch version). |
| Experiment Setup | Yes | For model training, we use the Adam optimizer(Kingma & Ba, 2015). The initial learning rate is 0.01 and will drop the learning rate by half every 100 epochs. The batch size is set to 128. ... For hyperparemeter in G-Mixup, we generate 20% more graphs for training graph. The graphons are estimated based on the training graphs. We use different λ [0.1, 0.2] to mix up the graphon and generate synthetic with different strength of mixing up. |