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..
Finding Global Homophily in Graph Neural Networks When Meeting Heterophily
Authors: Xiang Li, Renyu Zhu, Yao Cheng, Caihua Shan, Siqiang Luo, Dongsheng Li, Weining Qian
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments to compare our models against 11 other competitors on 15 benchmark datasets in a wide range of domains, scales and graph heterophilies. Experimental results show that our methods achieve superior performance and are also very ef๏ฌcient. |
| Researcher Affiliation | Collaboration | 1School of Data Science and Engineering, East China Normal University, Shanghai, China 2Microsoft Research Asia, Shanghai, China 3School of Computer Science and Engineering, Nanyang Technological University, Singapore. |
| Pseudocode | Yes | Finally, we summarize the pseudocodes of Glo GNN in Algorithm 1 (Section A of the appendix). |
| Open Source Code | Yes | We provide our code and data at https://github.com/RecklessRonan/GloGNN. |
| Open Datasets | Yes | For fairness, we conduct experiments on 15 benchmark datasets, which include 9 small-scale datasets released by (Pei et al., 2020) and 6 large-scale datasets from (Lim et al., 2021). We use the same training/validation/test splits as provided by the original papers. |
| Dataset Splits | Yes | We use the same training/validation/test splits as provided by the original papers. |
| Hardware Specification | Yes | Meanwhile, we run the experiments of 6 large-scale datasets on a single Tesla V100 GPU with 32G memory and use Adam W as the optimizer following (Lim et al., 2021). |
| Software Dependencies | No | The paper mentions "We implemented Glo GNN by Py Torch." but does not provide a specific version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | We perform a grid search to tune hyper-parameters based on the results on the validation set. Details of these hyper-parameters are listed in Tables 3 and 4. |