6 citations · 7 across the 4 of their papers we have counts for
4 papers
GPS: Graph Contrastive Learning via Multi-scale Augmented Views from Adversarial Pooling
Wei Ju, Yiyang Gu, Zhengyang Mao +5
Self-supervised graph representation learning has recently shown considerable promise in a range of fields, including bioinformatics and social networks. A large number of graph co…
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…
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…
Towards Long-Tailed Recognition for Graph Classification via Collaborative Experts
Siyu Yi, Zhengyang Mao, Wei Ju +4
Graph classification, aiming at learning the graph-level representations for effective class assignments, has received outstanding achievements, which heavily relies on high-qualit…