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..
LGI-GT: Graph Transformers with Local and Global Operators Interleaving
Authors: Shuo Yin, Guoqiang Zhong
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that LGI-GT performs consistently better than previous state-of-the-art GNNs and GTs, while ablation studies show the effectiveness of the proposed LGI scheme and EELA. |
| Researcher Affiliation | Academia | College of Computer Science and Technology, Ocean University of China EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Updating the embeddings of [CLS] |
| Open Source Code | Yes | The source code of LGI-GT is available at https://github.com/shuoyinn/LGI-GT. |
| Open Datasets | Yes | Among all the datasets we tested on, ZINC, PATTERN, CLUSTER were from [Dwivedi et al., 2020], whilst ogbg-molpcba and ogbg-code2 were from OGB [Hu et al., 2020a]. |
| Dataset Splits | Yes | Evaluation metrics and dataset splits were the same as in the original papers for each dataset. ... we took mean std of 10 runs with different random seeds |
| Hardware Specification | No | The paper does not specify the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies (e.g., programming languages, libraries, or frameworks). |
| Experiment Setup | Yes | On each dataset, we used the same number of hidden dimensions F and number of layers (or blocks) L as GPS. ... To achieve a fair comparison, m = n = 1 were constant (never tuned) across all the datasets. |