RoSA: A Robust Self-Aligned Framework for Node-Node Graph Contrastive Learning
Authors: Yun Zhu, Jianhao Guo, Fei Wu, Siliang Tang
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that Ro SA outperforms a series of graph contrastive learning frameworks on homophilous, non-homophilous and dynamic graphs, which validates the effectiveness of our work. We conduct extensive experiments on ten public benchmark datasets to evaluate the effectiveness of Ro SA. To prove the effectiveness of the design of Ro SA, we conduct ablation experiments masking different components under the same hyperparameters. |
| Researcher Affiliation | Academia | Yun Zhu , Jianhao Guo , Fei Wu and Siliang Tang Zhejiang University {zhuyun dcd,guojianhao,wufei,siliang}@zju.edu.cn |
| Pseudocode | Yes | We summarize our proposed algorithm for non-aligned node-node contrastive learning in Appendix A. |
| Open Source Code | Yes | Our codes are available at: https://github.com/Zhu Yun97/Ro SA |
| Open Datasets | Yes | We conduct experiments on ten public benchmark datasets that include four homophilous datasets (Cora, Citeseer, Pubmed and DBLP), three heterophilous datasets (Cornell, Wisconsin and Texas), two large-scale inductive datasets (Flickr and Reddit) and one dynamic graph dataset (CIAW) to evaluate the effectiveness of Ro SA. Details of datasets can be found in Appendix B. |
| Dataset Splits | No | The paper does not explicitly provide specific train/validation/test dataset splits (percentages or counts) or reference predefined splits with citations for reproduction in the main text. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, memory, or cloud instance types used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow, or other library versions). |
| Experiment Setup | No | Detailed hyperparameter settings are in Appendix C. |