KDLGT: A Linear Graph Transformer Framework via Kernel Decomposition Approach
Authors: Yi Wu, Yanyang Xu, Wenhao Zhu, Guojie Song, Zhouchen Lin, Liang Wang, Shaoguo Liu
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on both real-world and synthetic datasets indicate that KDLGT not only achieves state-of-the-art performance on various datasets but also reaches an acceleration ratio of approximately 10 on graphs of certain sizes. |
| Researcher Affiliation | Collaboration | Yi Wu1 , Yanyang Xu2 , Wenhao Zhu3 , Guojie Song3 , Zhouchen Lin3,4,5 , Liang Wang6 , Shaoguo Liu6 1 Academy for Advanced Interdisciplinary Studies, Peking University 2 School of Software and Microelectronics, Peking University 3 National Key Lab of General AI, School of Intelligence Science and Technology, Peking University 4 Institute for Artificial Intelligence, Peking University 5 Peng Cheng Laboratory 6 Alibaba Group, Beijing, China wuyi aais@pku.edu.cn , xuyanyang@stu.pku.edu.cn , {wenhaozhu, gjsong, zlin}@pku.edu.cn , {liangbo.wl, shaoguo.lsg}@alibaba-inc.com |
| Pseudocode | No | The paper describes methods using mathematical formulations but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any links to source code or explicitly state that the code is available or will be made public. |
| Open Datasets | Yes | The benchmarking-GNN [Dwivedi et al., 2020] (ZINC), OGB [Hu et al., 2020] (OGBG-MOLHIV) and TUD [Morris et al., 2020] (MUTAG, COX2 MD, PROTEINS, NCI1) are all popular graph-level benchmark datasets... The Cora, Citeseer and Pub Med [Yang et al., 2016] are popular citation datasets... Last FM-Asia [Rozemberczki and Sarkar, 2020] is a social network... |
| Dataset Splits | Yes | In the experiments, for the datasets without public splits, we use random split with the ratio of training/validation/test sets being 7/1.5/1.5. |
| Hardware Specification | Yes | All models are trained and evaluated on 3 NVIDIA RTX 3090 GPUs for the fairness of efficiency comparison. |
| Software Dependencies | No | The paper mentions 'Our models are implemented in Py Torch' but does not provide specific version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | We use Adam [Kingma and Ba, 2015] as the optimizer and set hyperparameter ϵ to 1e-7 and (β1, β2) to (0.99, 0.999), respectively. Besides, the initial learning rate is set to 1e-4 with a linear decay learning rate scheduler. The training and inference batch sizes are both set to 128. |