2 citations · 3 across the 26 of their papers we have counts for
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cs.LG2023★ 2 cited
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…
cs.LG2023★ 1 cited
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…
cs.LG2023
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…