LGI-GT: Graph Transformers with Local and Global Operators Interleaving
Authors: Shuo Yin, Guoqiang Zhong
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | 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 yinshuo@stu.ouc.edu.cn, gqzhong@ouc.edu.cn |
| 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. |