Graph Mixup on Approximate Gromov–Wasserstein Geodesics
Authors: Zhichen Zeng, Ruizhong Qiu, Zhe Xu, Zhining Liu, Yuchen Yan, Tianxin Wei, Lei Ying, Jingrui He, Hanghang Tong
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that the proposed GEOMIX promotes the generalization and robustness of GNN models. |
| Researcher Affiliation | Academia | 1University of Illinois Urbana-Champaign 2University of Michigan, Ann Arbor. |
| Pseudocode | Yes | Algorithm 1 GEOMIX |
| Open Source Code | Yes | Code and datasets are available at https://github.com/zhichenz98/Geo Mix-ICML24. |
| Open Datasets | Yes | All the real-world datasets used in the paper are from (Morris et al., 2020) and available online1. We give a brief introduction to the datasets as follows PROTEINS (Borgwardt et al., 2005), MUTAG (Kriege & Mutzel, 2012), MSRC-9 (Neumann et al., 2016), IMDB-B (Yanardag & Vishwanathan, 2015), and IMDB-M (Yanardag & Vishwanathan, 2015). |
| Dataset Splits | Yes | We split the dataset into train/test/validation set by 80%/10%/10% and use 10-fold cross validation for evaluation. |
| Hardware Specification | Yes | For model training, each model is trained for 300 epochs on the Linux platform with an Intel Xeon Gold 6240R CPU and an NVIDIA Tesla V100 SXM2 GPU. |
| Software Dependencies | No | The paper mentions 'implemented in Python' and 'built upon Py Torch' but does not specify version numbers for these software components. |
| Experiment Setup | Yes | For the GCN model (Kipf & Welling, 2017), we adopt three GCN layers with 32 hidden dimensions. We use Re Lu as the activation function and global mean pooling as the readout. A dropout layer with dropout probability p = 0.5 is appended after the GCN layers, and followed by a linear layer with softmax activation to map embeddings to the classification probability. |