108 citations · 108 across the 4 of their papers we have counts for
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cs.LG2026
Knowledge Graphs Meet Graph Neural Networks: A Comprehensive Survey
Chengcheng Sun, Jiayun Tian, Cheng Zhai +5
Graph Neural Networks (GNNs) have emerged as a powerful paradigm in Knowledge Graphs (KGs) due to their intrinsic ability to model graph-structured data. However, there remains a l…
cs.LG2026
GraphSB: Boosting Imbalanced Node Classification on Graphs through Structural Balance
Zhixiao Wang, Chaofan Zhu, Qihan Feng +3
Imbalanced node classification is a critical challenge in graph learning, where most existing methods typically utilize Graph Neural Networks (GNNs) to learn node representations.…
cs.LG2025
GraphSB: Boosting Imbalanced Node Classification on Graphs through Structural Balance
Chaofan Zhu, Xiaobing Rui, Zhixiao Wang
Imbalanced node classification is a critical challenge in graph learning, where most existing methods typically utilize Graph Neural Networks (GNNs) to learn node representations.…