Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Graph Mixup with Soft Alignments
Authors: Hongyi Ling, Zhimeng Jiang, Meng Liu, Shuiwang Ji, Na Zou
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct systematic experiments to show that S-Mixup can improve the performance and generalization of graph neural networks (GNNs) on various graph classification tasks. |
| Researcher Affiliation | Academia | 1Department of Computer Science & Engineering, Texas A&M University, TX, USA 2Department of Engineering Technology & Industrial Distribution, Texas A&M University, TX, USA. |
| Pseudocode | Yes | Algorithm 1 Training algorithm", "Algorithm 2 Mixup algorithm |
| Open Source Code | Yes | Our code is publicly available as part of the DIG package (https://github.com/divelab/DIG). |
| Open Datasets | Yes | In this section, we evaluate the effectiveness of our method on six real-world datasets from the TUDatasets benchmark (Morris et al., 2020)2... We also conduct experiments on ogbg-molhiv, which is a large molecular graph dataset from OGB benchmark (Hu et al., 2020)3. |
| Dataset Splits | Yes | For the TUDatasetes benchmark, we randomly split the dataset into train/validation/test data by 80%/10%/10%. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions using GCN and GIN models and the Adam optimizer, but does not provide specific software dependencies with version numbers (e.g., Python, PyTorch/TensorFlow, specific graph neural network libraries). |
| Experiment Setup | Yes | We use the Adam optimizer (Kingma & Ba, 2015) to train all models. See Table 8 for the hyperparameters of training the classification model. ... For the graph matching network used in S-Mixup, we set the hidden size as 256 and the readout layer as global sum pooling. For all six datasets, the graph matching network is trained for 500 epochs with a learning rate of 0.001. For the number of layers and batch size, see Table 9. |