30 citations · 47 across the 17 of their papers we have counts for
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cs.LG2023★ 30 cited
Efficient Heterogeneous Graph Learning via Random Projection
Jun Hu, Bryan Hooi, Bingsheng He
Heterogeneous Graph Neural Networks (HGNNs) are powerful tools for deep learning on heterogeneous graphs. Typical HGNNs require repetitive message passing during training, limiting…
cs.LG2023★ 6 cited
A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directions
Zemin Liu, Yuan Li, Nan Chen +3
Graphs represent interconnected structures prevalent in a myriad of real-world scenarios. Effective graph analytics, such as graph learning methods, enables users to gain profound…