CLEAR: Generative Counterfactual Explanations on Graphs
Authors: Jing Ma, Ruocheng Guo, Saumitra Mishra, Aidong Zhang, Jundong Li
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on both synthetic and real-world graphs validate the superiority of CLEAR over the state-of-the-art methods in different aspects. In this section, we evaluate our framework CLEAR with extensive experiments on both synthetic and real-world graphs. |
| Researcher Affiliation | Collaboration | Jing Ma University of Virginia Charlottesville, VA, USA jm3mr@virginia.edu Ruocheng Guo Bytedance AI Lab London, UK rguo.asu@gmail.com Saumitra Mishra J.P. Morgan AI Research London, UK saumitra.mishra@jpmorgan.com Aidong Zhang University of Virginia Charlottesville, VA, USA aidong@virginia.edu Jundong Li University of Virginia Charlottesville, VA, USA jundong@virginia.edu |
| Pseudocode | No | The paper describes the components and mechanism of CLEAR but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source code of our proposed framework is included in the supplemental material. It will be released after publication. |
| Open Datasets | Yes | We evaluate our method on three datasets, including a synthetic dataset and two datasets with realworld graphs. (1) Community. This dataset contains synthetic graphs generated by the Erdös-Rényi (E-R) model [24]. (2) Ogbg-molhiv. In this dataset, each graph stands for a molecule... (3) IMDB-M. This dataset contains movie collaboration networks from IMDB. In the ethics checklist, it states: 'The creators developed the adopted datasets based on either publicly available data or data with proper consent.' |
| Dataset Splits | No | The provided text indicates that data splits were specified in Appendix B ('Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] See Section 4, Appendix B and C.'), but the main body of the paper does not explicitly detail the training/validation/test dataset splits with percentages or sample counts. |
| Hardware Specification | No | The paper states that 'total amount of compute and the type of resources used' are in Appendix B ('Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [Yes] See Appendix B.'). However, no specific hardware details (e.g., GPU models, CPU types) are provided in the main text. |
| Software Dependencies | No | The paper does not provide specific software dependencies (e.g., library names with version numbers like PyTorch 1.9 or Python 3.8) needed to replicate the experiment in the provided text. |
| Experiment Setup | Yes | We set the desired label Y for each graph as its flipped label (e.g., if Y = 0, then Y = 1). For each graph, we generate three counterfactuals for it (N CF = 3). Other setup details are in Appendix B. We vary α {0.01, 0.1, 1.0, 5.0, 10.0} and β {0.01, 0.1, 1.0, 10.0, 100}. |