4 papers
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…
Scalable Fair Influence Blocking Maximization via Approximately Monotonic Submodular Optimization
Qiangpeng Fang, Jilong Shi, Xiaobin Rui +2
Influence Blocking Maximization (IBM) aims to select a positive seed set to suppress the spread of negative influence. However, existing IBM methods focus solely on maximizing bloc…
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.…
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.…