Search Results for author: Keyu Duan

Found 8 papers, 7 papers with code

GraphFM: A Comprehensive Benchmark for Graph Foundation Model

1 code implementation12 Jun 2024 Yuhao Xu, Xinqi Liu, Keyu Duan, Yi Fang, Yu-Neng Chuang, Daochen Zha, Qiaoyu Tan

To address these questions, we have constructed a rigorous benchmark that thoroughly analyzes and studies the generalization and scalability of self-supervised Graph Neural Network (GNN) models.

Graph Neural Network Link Prediction +3

SimTeG: A Frustratingly Simple Approach Improves Textual Graph Learning

2 code implementations3 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).

Feature Engineering Graph Learning +3

Contrastive Knowledge Graph Error Detection

1 code implementation18 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

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

1 code implementation24 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

2 code implementations20 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

3 code implementations7 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

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