Notice: 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. Full accuracy metrics and methodology are described in [1].
Single-View Graph Contrastive Learning with Soft Neighborhood Awareness
Authors: Qingqiang Sun, Chaoqi Chen, Ziyue Qiao, Xubin Zheng, Kai Wang
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on diverse node-level tasks demonstrate that our simple single-view GCL framework consistently outperforms existing methods by margins of up to 21.74% (PPI). We evaluate SIGNA on three kinds of node-level tasks, including transductive node classification, inductive node classification, and node clustering. |
| Researcher Affiliation | Academia | Qingqiang Sun1, Chaoqi Chen2, Ziyue Qiao1*, Xubin Zheng1, Kai Wang3, 1Great Bay University 2Shenzhen University 3Central South University EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes the methodology using mathematical formulas and descriptive text in the 'Methodology' section, but it does not include a clearly labeled pseudocode block or algorithm. |
| Open Source Code | Yes | Code https://github.com/sunisfighting/SIGNA |
| Open Datasets | Yes | Datasets. We comprehensively evaluate SIGNA on three kinds of node-level tasks across 7 datasets with various scales and properties (Velickovic et al. 2019; Jiao et al. 2020; Thakoor et al. 2021; Lee, Lee, and Park 2022). Wiki CS, Amazon Photo, Amazon Computers, Coauthor CS, and Coauthor Physics are used for transductive node classification and node clustering tasks. Two larger-scale datasets, Flickr and PPI, are used for inductive node classification on a single graph and multiple graphs, respectively. |
| Dataset Splits | Yes | Datasets. We comprehensively evaluate SIGNA on three kinds of node-level tasks across 7 datasets with various scales and properties (Velickovic et al. 2019; Jiao et al. 2020; Thakoor et al. 2021; Lee, Lee, and Park 2022). Wiki CS, Amazon Photo, Amazon Computers, Coauthor CS, and Coauthor Physics are used for transductive node classification and node clustering tasks. Two larger-scale datasets, Flickr and PPI, are used for inductive node classification on a single graph and multiple graphs, respectively. Statistics of these datasets are presented in the Appendix. |
| Hardware Specification | No | Implementation Details. Details about encoder implementation, hyperparameter selection, and computing infrastructure are provided in appendix due to the space limitation. |
| Software Dependencies | No | The paper mentions implementation details are in the appendix but does not specify any software names with version numbers in the main text. |
| Experiment Setup | No | Implementation Details. Details about encoder implementation, hyperparameter selection, and computing infrastructure are provided in appendix due to the space limitation. |