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..
Less is More: on the Over-Globalizing Problem in Graph Transformers
Authors: Yujie Xing, Xiao Wang, Yibo Li, Hai Huang, Chuan Shi
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on various graphs well validate the effectiveness of our proposed Co BFormer. Table 2 reports the experimental results on node classification. |
| Researcher Affiliation | Academia | 1School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China 2School of Software, Beihang University, Beijing, China. |
| Pseudocode | No | The paper describes the proposed method verbally and mathematically but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source code is available for reproducibility at: https://github.com/null-xyj/Co BFormer. |
| Open Datasets | Yes | We select seven datasets to evaluate, including homophilic graphs, i.e., Cora, Cite Seer, Pubmed (Yang et al., 2016), Ogbn-Arxiv, Ogbn-Products (Hu et al., 2020) and heterophilic graphs, i.e., Actor, Deezer (Lim et al., 2021b). |
| Dataset Splits | Yes | For Actor and Deezer, we perform five random splits of the nodes into train/valid/test sets, with the ratio of 50%:25%:25% (Lim et al., 2021b). |
| Hardware Specification | No | The paper mentions 'GPU memory' but does not provide specific hardware details such as GPU or CPU models, processor types, or memory amounts used for experiments. |
| Software Dependencies | No | The paper mentions software like Py G and Gamma GL but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | The hyperparameters are selected through grid search within the following search space: learning rate within {5e-4, 1e-3, 5e-3, 1e-2, 5e-2}. GCN layers within {2, 3}. weight decay of GCN within {1e-4, 5e-4, 1e-3, 5e-3, 1e-2}. weight decay of BGA within {1e-5, 5e-5, 1e-4, 5e-4, 1e-3}. |