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 embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Polynormer: Polynomial-Expressive Graph Transformer in Linear Time
Authors: Chenhui Deng, Zichao Yue, Zhiru Zhang
ICLR 2024 | Venue PDF | 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. |