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 [1].
Data-Augmented Curriculum Graph Neural Architecture Search under Distribution Shifts
Authors: Yang Yao, Xin Wang, Yijian Qin, Ziwei Zhang, Wenwu Zhu, Hong Mei
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on synthetic datasets and real datasets with distribution shifts demonstrate that our proposed method learns generalizable mappings and outperforms existing methods. |
| Researcher Affiliation | Academia | 1Department of Computer Science and Technology, Tsinghua University 2Beijing National Research Center for Information Science and Technology, Tsinghua University 3Mo E Key Lab of High Confidence Software Technologies, Peking University |
| Pseudocode | Yes | Algorithm 1: Generate new graphs with embedding guidance ... Algorithm 2: The overall searching algorithm of DCGAS |
| Open Source Code | No | The paper does not contain an explicit statement about open-sourcing the code or a link to a code repository. |
| Open Datasets | Yes | Spurious-Motif (Qin et al. 2022; Wu et al. 2022b; Ying et al. 2019) is a synthetic dataset. ... Ogbg-molhiv, Ogbg-molbace, Ogbg-molsider (Hu et al. 2020): they are molecular property prediction datasets... |
| Dataset Splits | No | The paper mentions 'training dataset' and 'testing dataset' but does not provide explicit details on the training/validation/test splits (e.g., percentages, sample counts, or specific predefined splits). |
| Hardware Specification | No | The paper does not explicitly describe the hardware specifications (e.g., specific GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not specify particular software dependencies with version numbers (e.g., Python version, specific library versions like PyTorch or TensorFlow). |
| Experiment Setup | No | The paper describes the overall method and evaluation but does not provide specific experimental setup details such as hyperparameter values (e.g., learning rate, batch size, number of epochs) or optimizer settings in the main text. |