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..
Unleashing the Power of Graph Data Augmentation on Covariate Distribution Shift
Authors: Yongduo Sui, Qitian Wu, Jiancan Wu, Qing Cui, Longfei Li, Jun Zhou, Xiang Wang, Xiangnan He
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments with in-depth empirical analysis demonstrate the superiority of our approach. The implementation codes are publicly available at https://github.com/yongduosui/AIA. In this section, we conduct extensive experiments to answer the following Research Questions: ... We first make comparisons with various baselines in Table 1, and have the following observations: ... We also conduct extensive experiments and in-depth analyses. |
| Researcher Affiliation | Collaboration | 1University of Science and Technology of China, 2Shanghai Jiao Tong University, 3Ant Group |
| Pseudocode | Yes | Algorithm 1: Estimation of Graph Covariate shift; Algorithm 2: Adversarial Invariant Augmentation |
| Open Source Code | Yes | The implementation codes are publicly available at https://github.com/yongduosui/AIA. |
| Open Datasets | Yes | We use graph OOD datasets [2] and OGB datasets [20], which include Motif, CMNIST, Molbbbp, and Molhiv. |
| Dataset Splits | Yes | For size covariate shift, we use small-size of graphs for training, while the validation and the test sets include the middle- and the large-size graphs, respectively. ... For scaffold shift, we follow [2] and use scaffold split to create training, validation and test sets. |
| Hardware Specification | Yes | We use the NVIDIA Ge Force RTX 3090 (24GB GPU) to conduct all our experiments. |
| Software Dependencies | No | The paper mentions using GIN as the backbone and discusses some general software aspects but does not provide specific version numbers for software dependencies (e.g., Python, PyTorch, or other libraries). |
| Experiment Setup | Yes | We tune the hyper-parameters in the following ranges: α and β {0.01,0.005,0.001}; λ2 {0.1,...,0.9}; γ {0.01,0.1,0.2,0.5,1.0,1.5,2.0,3.0,5.0}. The hyper-parameters are summarized in Table 5. |