2 citations · 3 across the 13 of their papers we have counts for
4 papers · 1 filter
VIGraph: Generative Self-supervised Learning for Class-Imbalanced Node Classification
Yulan Hu, Sheng Ouyang, Zhirui Yang +1
Class imbalance in graph data presents significant challenges for node classification. While existing methods, such as SMOTE-based approaches, partially mitigate this issue, they s…
Graph Ranking Contrastive Learning: A Extremely Simple yet Efficient Method
Yulan Hu, Sheng Ouyang, Jingyu Liu +6
Graph contrastive learning (GCL) has emerged as a representative graph self-supervised method, achieving significant success. The currently prevalent optimization objective for GCL…
Refining Latent Representations: A Generative SSL Approach for Heterogeneous Graph Learning
Yulan Hu, Zhirui Yang, Sheng Ouyang +1
Self-Supervised Learning (SSL) has shown significant potential and has garnered increasing interest in graph learning. However, particularly for generative SSL methods, its potenti…
Perfect Alignment May be Poisonous to Graph Contrastive Learning
Jingyu Liu, Huayi Tang, Yong Liu
Graph Contrastive Learning (GCL) aims to learn node representations by aligning positive pairs and separating negative ones. However, few of researchers have focused on the inner l…