Robust Counterfactual Explanations on Graph Neural Networks
Authors: Mohit Bajaj, Lingyang Chu, Zi Yu Xue, Jian Pei, Lanjun Wang, Peter Cho-Ho Lam, Yong Zhang
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Exhaustive experiments on many public datasets demonstrate the superior performance of our method. Last, we conduct comprehensive experimental study to compare our method with the state-of-the-art methods on fidelity, robustness, accuracy and efficiency. All the results solidly demonstrate the superior performance of our approach. |
| Researcher Affiliation | Collaboration | 1Huawei Technologies Canada Co., Ltd. 2Mc Master University 3 The University of British Columbia 4Simon Fraser University |
| Pseudocode | No | No structured pseudocode or algorithm blocks are provided in the paper. |
| Open Source Code | Yes | The code3 is publicly available. Code available at https://marketplace.huaweicloud.com/markets/aihub/notebook/detail/?id=e41f63d3-e346-4891-bf6a-40e64b4a3278 |
| Open Datasets | Yes | For the graph classification task, we use one synthetic dataset, BA-2motifs [25], and two real-world datasets, Mutagenicity [21] and NCI1 [39]. |
| Dataset Splits | No | The paper states, 'Please refer to Appendix E for details on datasets, baselines and the experiment setups,' indicating that specific dataset split information is not in the main text. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments are provided in the paper. |
| Software Dependencies | No | No specific software versions or library dependencies are provided for the ancillary software used in the experiments. |
| Experiment Setup | No | The paper mentions that hyperparameters and experimental setups are discussed in Appendices E and G, implying these details are not present in the main body of the paper. |