A Centrality-based Graph Learning Framework
Authors: Jiajun Yu, Zhihao Wu, Jielong Lu, Tianyue Wang, Haishuai Wang
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This section presents experiments to evaluate our proposed methods. We first introduce experimental settings, followed by performance analysis on graph classification and representation learning tasks. We also conduct ablation studies and provide visualizations. |
| Researcher Affiliation | Academia | 1College of Computer Science and Technology, Zhejiang University, China 2Shanghai Innovation Institute, Shanghai, China 3Innovation Institute for Artificial Intelligence in Medicine, College of Pharmaceutical Sciences, Zhejiang University, China |
| Pseudocode | No | The paper describes methods using mathematical formulations and descriptive text, but does not include a dedicated pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-sourcing the code for the described methodology. |
| Open Datasets | Yes | All datasets were collected from the TU datasets [Morris et al., 2020] and open graph benchmark repositories [Hu et al., 2020]. |
| Dataset Splits | No | The main text states 'Detailed experimental settings are provided in the Appendix B.', but the appendix is not included in the provided text. Therefore, specific dataset split information is not available in the main body of the paper. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments in the main text. It refers to 'Appendix B.5' for detailed training times, but hardware specifications are not directly mentioned. |
| Software Dependencies | No | The paper refers to 'Appendix B' for 'Additional experimental settings and implementation details' but does not specify any software dependencies with version numbers in the main text. |
| Experiment Setup | No | The paper describes the 'Parameter Settings' regarding the centrality measures used, but defers 'Additional experimental settings and implementation details' to 'Appendix B' without providing specific hyperparameters or system-level training settings in the main text. |