Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
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 | Input Tokens: 16,727 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,004 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |