120 citations · 132 across the 14 of their papers we have counts for
5 papers · 1 filter
A Survey on GNN-based Link Prediction: Techniques, Applications, and Challenges
Chengcheng Sun, Yajie Song, Cheng Zhai +6
Graph Neural Networks (GNNs) have emerged as the leading paradigm for link prediction, enabling the inference of missing connections and the anticipation of potential future links.…
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…
Attention-based graph neural networks: a survey
Chengcheng Sun, Chenhao Li, Xiang Lin +4
Graph neural networks (GNNs) aim to learn well-trained representations in a lower-dimension space for downstream tasks while preserving the topological structures. In recent years,…
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.…