Contrastive Knowledge Graph Error Detection

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).

Contrastive Learning

A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking

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).

Node Classification Node Property Prediction

Bag of Tricks for Training Deeper Graph Neural Networks: A Comprehensive Benchmark Study

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.

Transfer Learning Toolkit: Primers and Benchmarks

1 code implementation • 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.

Transfer Learning

A Comprehensive Survey on Transfer Learning

2 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.

Transfer Learning