8 citations · 12 across the 5 of their papers we have counts for
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cs.LG2025★ 1 cited
THeGCN: Temporal Heterophilic Graph Convolutional Network
Yuchen Yan, Yuzhong Chen, Huiyuan Chen +6
Graph Neural Networks (GNNs) have exhibited remarkable efficacy in diverse graph learning tasks, particularly on static homophilic graphs. Recent attention has pivoted towards more…
cs.LG2021★ 8 cited
Deep Active Learning by Leveraging Training Dynamics
Haonan Wang, Wei Huang, Ziwei Wu +3
Active learning theories and methods have been extensively studied in classical statistical learning settings. However, deep active learning, i.e., active learning with deep learni…
cs.LG2021
Graph Sanitation with Application to Node Classification
Zhe Xu, Boxin Du, Hanghang Tong
The past decades have witnessed the prosperity of graph mining, with a multitude of sophisticated models and algorithms designed for various mining tasks, such as ranking, classifi…