10 citations · 20 across the 3 of their papers we have counts for
3 papers
cs.LG2024★ 2 cited
UGMAE: A Unified Framework for Graph Masked Autoencoders
Yijun Tian, Chuxu Zhang, Ziyi Kou +3
Generative self-supervised learning on graphs, particularly graph masked autoencoders, has emerged as a popular learning paradigm and demonstrated its efficacy in handling non-Eucl…
cs.LG2023★ 10 cited
Class-Imbalanced Learning on Graphs: A Survey
Yihong Ma, Yijun Tian, Nuno Moniz +1
The rapid advancement in data-driven research has increased the demand for effective graph data analysis. However, real-world data often exhibits class imbalance, leading to poor p…
cs.LG2023★ 8 cited
Knowledge Distillation on Graphs: A Survey
Yijun Tian, Shichao Pei, Xiangliang Zhang +2
Graph Neural Networks (GNNs) have attracted tremendous attention by demonstrating their capability to handle graph data. However, they are difficult to be deployed in resource-limi…