47 citations · 48 across the 5 of their papers we have counts for
5 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…
Dynamic Hypergraph Structure Learning for Traffic Flow Forecasting
Yusheng Zhao, Xiao Luo, Wei Ju +3
This paper studies the problem of traffic flow forecasting, which aims to predict future traffic conditions on the basis of road networks and traffic conditions in the past. The pr…
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…
Towards Semi-supervised Universal Graph Classification
Xiao Luo, Yusheng Zhao, Yifang Qin +2
Graph neural networks have pushed state-of-the-arts in graph classifications recently. Typically, these methods are studied within the context of supervised end-to-end training, wh…