4 papers
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…
Improving Signed Propagation for Graph Neural Networks in Multi-Class Environments
Yoonhyuk Choi, Jiho Choi, Taewook Ko +1
Message-passing Graph Neural Networks (GNNs), which collect information from adjacent nodes achieve dismal performance on heterophilic graphs. Various schemes have been proposed to…