6 papers
Accelerating Storage-Based Training for Graph Neural Networks
Myung-Hwan Jang, Jeong-Min Park, Yunyong Ko +1
Graph neural networks (GNNs) have achieved breakthroughs in various real-world downstream tasks due to their powerful expressiveness. As the scale of real-world graphs has been con…
Learning Short-Term and Long-Term Patterns of High-Order Dynamics in Real-World Networks
Yunyong Ko, Da Eun Lee, Song Kyung Yu +1
Real-world networks have high-order relationships among objects and they evolve over time. To capture such dynamics, many works have been studied in a range of fields. Via an in-de…
Is This News Still Interesting to You?: Lifetime-aware Interest Matching for News Recommendation
Seongeun Ryu, Yunyong Ko, Sang-Wook Kim
Personalized news recommendation aims to deliver news articles aligned with users' interests, serving as a key solution to alleviate the problem of information overload on online n…
HyGEN: Regularizing Negative Hyperedge Generation for Accurate Hyperedge Prediction
Song Kyung Yu, Da Eun Lee, Yunyong Ko +1
Hyperedge prediction is a fundamental task to predict future high-order relations based on the observed network structure. Existing hyperedge prediction methods, however, suffer fr…
CROWN: A Novel Approach to Comprehending Users' Preferences for Accurate Personalized News Recommendation
Yunyong Ko, Seongeun Ryu, Sang-Wook Kim
Personalized news recommendation aims to assist users in finding news articles that align with their interests, which plays a pivotal role in mitigating users' information overload…
Enhancing Hyperedge Prediction with Context-Aware Self-Supervised Learning
Yunyong Ko, Hanghang Tong, Sang-Wook Kim
Hypergraphs can naturally model group-wise relations (e.g., a group of users who co-purchase an item) as hyperedges. Hyperedge prediction is to predict future or unobserved hypered…