40 citations · 121 across the 43 of their papers we have counts for
5 papers · 1 filter
Poincaré Differential Privacy for Hierarchy-Aware Graph Embedding
Yuecen Wei, Haonan Yuan, Xingcheng Fu +4
Hierarchy is an important and commonly observed topological property in real-world graphs that indicate the relationships between supervisors and subordinates or the organizational…
Environment-Aware Dynamic Graph Learning for Out-of-Distribution Generalization
Haonan Yuan, Qingyun Sun, Xingcheng Fu +4
Dynamic graph neural networks (DGNNs) are increasingly pervasive in exploiting spatio-temporal patterns on dynamic graphs. However, existing works fail to generalize under distribu…
Does Graph Distillation See Like Vision Dataset Counterpart?
Beining Yang, Kai Wang, Qingyun Sun +5
Training on large-scale graphs has achieved remarkable results in graph representation learning, but its cost and storage have attracted increasing concerns. Existing graph condens…
Hyperbolic Geometric Graph Representation Learning for Hierarchy-imbalance Node Classification
Xingcheng Fu, Yuecen Wei, Qingyun Sun +4
Learning unbiased node representations for imbalanced samples in the graph has become a more remarkable and important topic. For the graph, a significant challenge is that the topo…
Unbiased and Efficient Self-Supervised Incremental Contrastive Learning
Cheng Ji, Jianxin Li, Hao Peng +4
Contrastive Learning (CL) has been proved to be a powerful self-supervised approach for a wide range of domains, including computer vision and graph representation learning. Howeve…