10 citations · 10 across the 2 of their papers we have counts for
3 papers
cs.LG2026
Rethinking Efficient Graph Coarsening via a Non-Selfishness Principle
Xu Bai, Bin Lu, Kun Zhang +4
Graph coarsening is a graph dimensionality reduction technique that aims to construct a smaller and more tractable graph while preserving the essential structural and semantic prop…
stat.ML2026
CGRL: Causal-Guided Representation Learning for Node-Level Out-of-Distribution Generalization
Bowen Lu, Lianqiang Yang, Teng Li +1
Graph Neural Networks (GNNs) deliver strong performance on graph tasks, but their accuracy drops significantly under out-of-distribution (OOD) scenarios. Under distribution shifts,…
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…