4 citations · 4 across the 2 of their papers we have counts for
2 papers
cs.LG2024
Loss-aware Curriculum Learning for Heterogeneous Graph Neural Networks
Zhen Hao Wong, Hansi Yang, Xiaoyi Fu +1
Heterogeneous Graph Neural Networks (HGNNs) are a class of deep learning models designed specifically for heterogeneous graphs, which are graphs that contain different types of nod…
cs.LG2023★ 4 cited
Positive-Unlabeled Node Classification with Structure-aware Graph Learning
Hansi Yang, Yongqi Zhang, Quanming Yao +1
Node classification on graphs is an important research problem with many applications. Real-world graph data sets may not be balanced and accurate as assumed by most existing works…