22 citations · 23 across the 11 of their papers we have counts for
4 papers · 1 filter
Gauge-Equivariant Graph Networks via Self-Interference Cancellation
Yoonhyuk Choi, Jiho Choi, Jiwoo Kang
Graph Neural Networks (GNNs) excel on homophilous graphs but often fail under heterophily due to self-reinforcing and phase-inconsistent signals. We propose a \textbf{G}auge-\textb…
Sheaf Graph Neural Networks via PAC-Bayes Spectral Optimization
Yoonhyuk Choi, Jiho Choi, Chong-Kwon Kim
Over-smoothing in Graph Neural Networks (GNNs) causes collapse in distinct node features, particularly on heterophilic graphs where adjacent nodes often have dissimilar labels. Alt…
Hierarchical Uncertainty-Aware Graph Neural Network
Yoonhyuk Choi, Jiho Choi, Taewook Ko +1
Recent research on graph neural networks (GNNs) has explored mechanisms for capturing local uncertainty and exploiting graph hierarchies to mitigate data sparsity and leverage stru…
Adaptive Branch Specialization in Spectral-Spatial Graph Neural Networks for Certified Robustness
Yoonhyuk Choi, Jiho Choi, Chong-Kwon Kim
Recent Graph Neural Networks (GNNs) combine spectral-spatial architectures for enhanced representation learning. However, limited attention has been paid to certified robustness, p…