Uncertainty-guided Graph Contrastive Learning from a Unified Perspective
Authors: Zhiqiang Li, Jie Wang, Jianqing Liang, Junbiao Cui, Xingwang Zhao, Jiye Liang
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This section evaluates the proposed method by detailing the datasets, experimental settings, and assessing model performance in key tasks such as node classification, node clustering, and link prediction. It also presents findings from ablation studies and hyperparameter analysis. |
| Researcher Affiliation | Academia | 1Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, School of Computer and Information Technology, Shanxi University 2School of Computer Science and Technology, Taiyuan University of Science and Technology EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The entire algorithm is shown in Appendix A. Appendix A is not provided in the extracted text, thus no pseudocode is available in the main body. |
| Open Source Code | No | No explicit statement or link for open-source code release is found in the provided paper text. |
| Open Datasets | Yes | This paper evaluates the proposed method using six commonly used datasets, including four citation networks (Cora [Sen et al., 2008], Citeseer [Sen et al., 2008], Pubmed [Sen et al., 2008], and DBLP [Yang and Leskovec, 2012]) and two Amazon co-purchase networks (Amazon-Computers [Shchur et al., 2018] and Amazon-Photo [Shchur et al., 2018]). |
| Dataset Splits | Yes | For each dataset, we follow the experimental setup of GCA [Zhu et al., 2021], 10%, 10%, and 80% of the nodes were allocated for training, validation, and testing, respectively. |
| Hardware Specification | No | The paper mentions that the algorithm was implemented using Python and PyTorch, but does not provide any specific hardware details such as CPU, GPU models, or memory. |
| Software Dependencies | No | In the experiments, the algorithm proposed in this paper was implemented using the Python programming language and the Py Torch framework. Specific version numbers for Python or PyTorch are not provided. |
| Experiment Setup | No | The paper discusses hyperparameter analysis in Section 4.6 (e.g., 'The Number of Views M and Edge Retention Ratio K') but does not explicitly state the specific hyperparameter values (e.g., learning rate, batch size, number of epochs, optimizer settings) used for the main experiments in the 'Experimental Setup' section or elsewhere. |