22 citations · 34 across the 10 of their papers we have counts for
10 papers
Persistent Tri-State Message Passing
Yoonhyuk Choi, Jiho Choi, Chong-Kwon Kim +6
In stochastic message passing, an edge's sampled role changes the node states used to compute adaptive weights at later layers. Weight averaging therefore depends on whether edge r…
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…
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…
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…
Finding Heterophilic Neighbors via Confidence-based Subgraph Matching for Semi-supervised Node Classification
Yoonhyuk Choi, Jiho Choi, Taewook Ko +1
Graph Neural Networks (GNNs) have proven to be powerful in many graph-based applications. However, they fail to generalize well under heterophilic setups, where neighbor nodes have…
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…
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)…
PU GNN: Chargeback Fraud Detection in P2E MMORPGs via Graph Attention Networks with Imbalanced PU Labels
Jiho Choi, Junghoon Park, Woocheol Kim +3
The recent advent of play-to-earn (P2E) systems in massively multiplayer online role-playing games (MMORPGs) has made in-game goods interchangeable with real-world values more than…
Review-Based Domain Disentanglement without Duplicate Users or Contexts for Cross-Domain Recommendation
Yoonhyuk Choi, Jiho Choi, Taewook Ko +2
A cross-domain recommendation has shown promising results in solving data-sparsity and cold-start problems. Despite such progress, existing methods focus on domain-shareable inform…