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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Towards Unsupervised Open-Set Graph Domain Adaptation via Dual Reprogramming
Authors: Zhen Zhang, Bingsheng He
NeurIPS 2025 | Venue PDF | LLM Run Details | Input Tokens: 27,466 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,983 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 | 4 Experiments 4.1 Experimental Settings 4.2 Results and Analyses 4.3 Ablation Studies |
| Researcher Affiliation | Academia | Zhen Zhang1,2 1Nanjing University 2National University of Singapore EMAIL Bingsheng He National University of Singapore Singapore EMAIL |
| Pseudocode | Yes | Algorithm 1 Graph RTA s Training Strategy |
| Open Source Code | Yes | Our source codes and datasets are publicly available at https://github.com/cszhangzhen/Graph RTA. |
| Open Datasets | Yes | Our source codes and datasets are publicly available at https://github.com/cszhangzhen/Graph RTA. To thoroughly assess the performance of our proposed Graph RTA, we conduct experiments using three categories of publicly available datasets. An overview of these dataset characteristics is provided in Table 1 |
| Dataset Splits | Yes | Among them, 70% of the labeled source nodes are utilized for training, 10% are set aside for validation, and the remaining 20% serve as a sanity check. The ο¬nal evaluation is conducted on the target nodes. |
| Hardware Specification | Yes | Our experiments are conducted on a Linux server with 2 AMD EPYC 7543 CPU@2.80GHz, 512G RAM and one NVIDIA A100-SXM4-80GB GPU. |
| Software Dependencies | Yes | The proposed model is implemented with Pytorch 1.13.1 in Python 3.8 using Pytorch Geometric 2.4.0. |
| Experiment Setup | Yes | Hyperparameters for learning rate, weight decay and Ξ» are searched within the ranges of [0.1, 0.01, 0.001, 1e 4, 1e 5], and the sparse constraint Ο is explored within the interval [0, 1]. The experiments are repeated ο¬ve times, and performance metrics are reported as the mean along with standard deviations for both accuracy and H-score [10]. |