Comparing Graph Transformers via Positional Encodings
Authors: Mitchell Black, Zhengchao Wan, Gal Mishne, Amir Nayyeri, Yusu Wang
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | C. Experiments In this section, we carry out experiments to validate our two main results Theorem 3.8 and Theorem 3.10. Our code is adapted from the Graph GPS module (Rampasek et al., 2022) and subsequent fork from Muller et al. (2024). C.1. Graph Isomorphism: CSL Table 1. Test performance on the CSL dataset of different APEs. |
| Researcher Affiliation | Academia | 1School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, Oregon, USA 2Halıcıoˇglu Data Science Institute, University of California San Diego, San Diego, California, USA. |
| Pseudocode | No | The paper describes algorithms (e.g., Weisfeiler-Lehman algorithm) in prose rather than structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code for all experiments can be found at https://github.com/blackmit/comparing_graph_transformers_via_positional_encodings |
| Open Datasets | Yes | C.1. Graph Isomorphism: CSL (...) We consider the graph isomorphism benchmark dataset BREC (Wang & Zhang, 2024). (...) In this experiment, we compare RPE-GTs and EGN APE-GTS for graph regression on the small ZINC dataset containing 12k graphs (Dwivedi et al., 2023). |
| Dataset Splits | No | The paper refers to 'test performance' on datasets but does not explicitly state the training, validation, and test dataset splits (e.g., percentages or specific counts) needed for reproduction. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, processor types, or memory amounts) used for running experiments are explicitly provided in the paper. |
| Software Dependencies | Yes | Table 6. Hyperparameters for ZINC Experiment (...) Python 3.8, PyTorch 1.9, and CUDA 11.1 |
| Experiment Setup | Yes | Table 6. Hyperparameters for ZINC Experiment (...) # Transformer Layers: 14 # Transformer Heads: 8 # Gaussian Kernels 16 # MLP Layers 2 MLP Hidden Dimension (No Edge Features) 16 MLP Hidden Dimension (Edge Features) 16 EGN APE GTs # Transformer Layers: 8 # Transformer Heads: 8 # EGN Layers 6 EGN Hidden Dim (No Edge Features) 48 EGN Hidden Dim (Edge Features) 64 APE Type Add SPE # Deep Sets Layers 3 Deep Sets Hidden Dimension 64 # Parameters 17217 |