10 citations · 20 across the 3 of their papers we have counts for
3 papers
cs.LG2024
Empowering Graph Invariance Learning with Deep Spurious Infomax
Tianjun Yao, Yongqiang Chen, Zhenhao Chen +3
Recently, there has been a surge of interest in developing graph neural networks that utilize the invariance principle on graphs to generalize the out-of-distribution (OOD) data. D…
cs.LG2024★ 10 cited
MuGSI: Distilling GNNs with Multi-Granularity Structural Information for Graph Classification
Tianjun Yao, Jiaqi Sun, Defu Cao +2
Recent works have introduced GNN-to-MLP knowledge distillation (KD) frameworks to combine both GNN's superior performance and MLP's fast inference speed. However, existing KD frame…
cs.LG2024★ 10 cited
Improving the Expressiveness of -hop Message-Passing GNNs by Injecting Contextualized Substructure Information
Tianjun Yao, Yiongxu Wang, Kun Zhang +1
Graph neural networks (GNNs) have become the \textit{de facto} standard for representational learning in graphs, and have achieved state-of-the-art performance in many graph-relate…