227 citations · 311 across the 8 of their papers we have counts for
8 papers
Logical Reasoning with Relation Network for Inductive Knowledge Graph Completion
Qinggang Zhang, Keyu Duan, Junnan Dong +2
Inductive knowledge graph completion (KGC) aims to infer the missing relation for a set of newly-coming entities that never appeared in the training set. Such a setting is more in…
GraphFM: A Comprehensive Benchmark for Graph Foundation Model
Yuhao Xu, Xinqi Liu, Keyu Duan +4
Foundation Models (FMs) serve as a general class for the development of artificial intelligence systems, offering broad potential for generalization across a spectrum of downstream…
SimTeG: A Frustratingly Simple Approach Improves Textual Graph Learning
Keyu Duan, Qian Liu, Tat-Seng Chua +4
Textual graphs (TGs) are graphs whose nodes correspond to text (sentences or documents), which are widely prevalent. The representation learning of TGs involves two stages: (i) uns…
Contrastive Knowledge Graph Error Detection
Qinggang Zhang, Junnan Dong, Keyu Duan +3
Knowledge Graph (KG) errors introduce non-negligible noise, severely affecting KG-related downstream tasks. Detecting errors in KGs is challenging since the patterns of errors are…
A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking
Keyu Duan, Zirui Liu, Peihao Wang +5
Large-scale graph training is a notoriously challenging problem for graph neural networks (GNNs). Due to the nature of evolving graph structures into the training process, vanilla…
Bag of Tricks for Training Deeper Graph Neural Networks: A Comprehensive Benchmark Study
Tianlong Chen, Kaixiong Zhou, Keyu Duan +4
Training deep graph neural networks (GNNs) is notoriously hard. Besides the standard plights in training deep architectures such as vanishing gradients and overfitting, it also uni…
Transfer Learning Toolkit: Primers and Benchmarks
Fuzhen Zhuang, Keyu Duan, Tongjia Guo +4
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. It…
A Comprehensive Survey on Transfer Learning
Fuzhen Zhuang, Zhiyuan Qi, Keyu Duan +5
Transfer learning aims at improving the performance of target learners on target domains by transferring the knowledge contained in different but related source domains. In this wa…