5 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…
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,…
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.…
Time-Critical Adversarial Influence Blocking Maximization
Jilong Shi, Qiangpeng Fang, Xiaobin Rui +2
Adversarial Influence Blocking Maximization (AIBM) aims to select a set of positive seed nodes that propagate synchronously with the known negative seed nodes to counteract their n…
Efficient Approximation Algorithms for Fair Influence Maximization under Maximin Constraint
Xiaobin Rui, Qiangpeng Fang, Chen Peng +3
Fair Influence Maximization (FIM) seeks to mitigate disparities in influence across different groups and has recently garnered increasing attention. A widely adopted notion of fair…