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 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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. |