7 papers
Sparse Bayesian Message Passing under Structural Uncertainty
Yoonhyuk Choi, Jiho Choi, Chanran Kim +5
Semi-supervised learning on real-world graphs is frequently challenged by heterophily, where the observed graph is unreliable or label-disassortative. Many existing graph neural ne…
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…
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…
Review-Based Hyperbolic Cross-Domain Recommendation
Yoonhyuk Choi, Jiho Choi, Taewook Ko +1
The issue of data sparsity poses a significant challenge to recommender systems. In response to this, algorithms that leverage side information such as review texts have been propo…
Mitigating Overfitting in Graph Neural Networks via Feature and Hyperplane Perturbation
Yoonhyuk Choi, Jiho Choi, Taewook Ko +1
Graph neural networks (GNNs) are commonly used in semi-supervised settings. Previous research has primarily focused on finding appropriate graph filters (e.g. aggregation methods)…
Better Not to Propagate: Understanding Edge Uncertainty and Over-smoothing in Signed Graph Neural Networks
Yoonhyuk Choi, Jiho Choi, Taewook Ko +1
Traditional Graph Neural Networks (GNNs) rely on network homophily, which can lead to performance degradation due to over-smoothing in many real-world heterophily scenarios. Recent…