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..
On the Stability of Expressive Positional Encodings for Graphs
Authors: Yinan Huang, William Lu, Joshua Robinson, Yu Yang, Muhan Zhang, Stefanie Jegelka, Pan Li
ICLR 2024 | Venue PDF | LLM Run Details | Input Tokens: 33,577 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,307 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 | Finally, we evaluate the effectiveness of our method on molecular property prediction, and out-of-distribution generalization tasks, finding improved generalization compared to existing positional encoding methods. Our code is available at https://github.com/Graph-COM/SPE. |
| Researcher Affiliation | Academia | 1Georgia Institute of Technology, 2Purdue University, 3Stanford University, 4Tongji University, 5Peking University, 6 MIT CSAIL |
| Pseudocode | No | The paper describes the SPE architecture mathematically and illustrates it in Figure 1, but it does not include a formal pseudocode block or algorithm labeled as such. |
| Open Source Code | Yes | Our code is available at https://github.com/Graph-COM/SPE. |
| Open Datasets | Yes | We primarily use three datasets: ZINC (Dwivedi et al., 2023), Alchemy (Chen et al., 2019) and Drug OOD (Ji et al., 2023). |
| Dataset Splits | Yes | For each domain, the full dataset is divided into five partitions: the training set, the in-distribution (ID) validation/test sets, the out-of-distribution validation/test sets. ... For each task, we randomly split dataset into training, validation and test by 8:1:1. |
| Hardware Specification | Yes | GPU is Quadro RTX 6000 |
| Software Dependencies | No | The paper mentions software components like PyTorch, GIN, Deep Set, MLPs, Adam optimizer, and ReLU activations, but does not provide specific version numbers for them. |
| Experiment Setup | Yes | We use Adam optimizer with an initial learning rate 0.001 and 100 warm-up steps. We adopt a linear decay learning rate scheduler. Batch size is 128 for ZINC, Alchemy and substructures counting, 64 for Drug OOD. |