Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
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 | Venue PDF | LLM Run Details | Input Tokens: 18,823 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,882 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |