2 code implementations • 3 Aug 2023 • Keyu Duan, Qian Liu, Tat-Seng Chua, Shuicheng Yan, Wei Tsang Ooi, Qizhe Xie, Junxian He
More recently, with the rapid development of language models (LMs), researchers have focused on leveraging LMs to facilitate the learning of TGs, either by jointly training them in a computationally intensive framework (merging the two stages), or designing complex self-supervised training tasks for feature extraction (enhancing the first stage).
Ranked #1 on Node Property Prediction on ogbn-arxiv
1 code implementation • 18 Nov 2022 • Qinggang Zhang, Junnan Dong, Keyu Duan, Xiao Huang, Yezi Liu, Linchuan Xu
To this end, we propose a novel framework - ContrAstive knowledge Graph Error Detection (CAGED).
2 code implementations • 14 Oct 2022 • Keyu Duan, Zirui Liu, Peihao Wang, Wenqing Zheng, Kaixiong Zhou, Tianlong Chen, Xia Hu, Zhangyang Wang
Large-scale graph training is a notoriously challenging problem for graph neural networks (GNNs).
Ranked #2 on Node Property Prediction on ogbn-products
1 code implementation • 24 Aug 2021 • Tianlong Chen, Kaixiong Zhou, Keyu Duan, Wenqing Zheng, Peihao Wang, Xia Hu, Zhangyang Wang
In view of those, we present the first fair and reproducible benchmark dedicated to assessing the "tricks" of training deep GNNs.
2 code implementations • 20 Nov 2019 • Fuzhen Zhuang, Keyu Duan, Tongjia Guo, Yongchun Zhu, Dongbo Xi, Zhiyuan Qi, Qing He
The transfer learning toolkit wraps the codes of 17 transfer learning models and provides integrated interfaces, allowing users to use those models by calling a simple function.
3 code implementations • 7 Nov 2019 • Fuzhen Zhuang, Zhiyuan Qi, Keyu Duan, Dongbo Xi, Yongchun Zhu, HengShu Zhu, Hui Xiong, Qing He
In order to show the performance of different transfer learning models, over twenty representative transfer learning models are used for experiments.