2 citations · 5 across the 4 of their papers we have counts for
4 papers
ALEX: Towards Effective Graph Transfer Learning with Noisy Labels
Jingyang Yuan, Xiao Luo, Yifang Qin +3
Graph Neural Networks (GNNs) have garnered considerable interest due to their exceptional performance in a wide range of graph machine learning tasks. Nevertheless, the majority of…
Redundancy-Free Self-Supervised Relational Learning for Graph Clustering
Si-Yu Yi, Wei Ju, Yifang Qin +4
Graph clustering, which learns the node representations for effective cluster assignments, is a fundamental yet challenging task in data analysis and has received considerable atte…
RAHNet: Retrieval Augmented Hybrid Network for Long-tailed Graph Classification
Zhengyang Mao, Wei Ju, Yifang Qin +2
Graph classification is a crucial task in many real-world multimedia applications, where graphs can represent various multimedia data types such as images, videos, and social netwo…
Transferable Cross-Tokamak Disruption Prediction with Deep Hybrid Neural Network Feature Extractor
Wei Zheng, Fengming Xue, Ming Zhang +12
Predicting disruptions across different tokamaks is a great obstacle to overcome. Future tokamaks can hardly tolerate disruptions at high performance discharge. Few disruption disc…