Polynormer: Polynomial-Expressive Graph Transformer in Linear Time
Authors: Chenhui Deng, Zichao Yue, Zhiru Zhang
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Polynormer has been evaluated on 13 homophilic and heterophilic datasets, including large graphs with millions of nodes. Our extensive experiment results show that Polynormer outperforms state-of-the-art GNN and GT baselines on most datasets, even without the use of nonlinear activation functions. |
| Researcher Affiliation | Academia | Chenhui Deng, Zichao Yue, Zhiru Zhang Cornell University, Ithaca, USA |
| Pseudocode | Yes | Code 1: Py Torch-style Pseudocode for Polynormer |
| Open Source Code | Yes | Source code of Polynormer is freely available at: github.com/cornell-zhang/Polynormer. |
| Open Datasets | Yes | Appendix E shows the details of all 13 datasets used in our experiments, which consist of 7 homophilic and 6 heterophilic graphs. For Computer, Photo, CS, and Physics datasets, we adhere to the widely accepted practice of randomly dividing nodes into training (60%), validation (20%), and test (20%) sets (Chen et al., 2022b; Shirzad et al., 2023). |
| Dataset Splits | Yes | For Computer, Photo, CS, and Physics datasets, we adhere to the widely accepted practice of randomly dividing nodes into training (60%), validation (20%), and test (20%) sets (Chen et al., 2022b; Shirzad et al., 2023). |
| Hardware Specification | Yes | We conduct all experiments on a Linux machine equipped with an Intel Xeon Gold 5218 CPU (featuring 8 cores @ 2.30 GHz) and 4 RTX A6000 GPUs (each with 48 GB of memory). |
| Software Dependencies | No | The paper mentions 'Py G (Fey & Lenssen, 2019) and DGL (Wang et al., 2019)' as frameworks but does not specify exact version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | Detailed hyperparameter settings of baselines and Polynormer are available in Appendix H. In Table 5, we provide the critical hyperparameters of Polynormer used with each dataset. |