3 papers
cs.LG2026
TaLK: Text-attributed Graph Dataset Distillation via Coupling Language Model with Graph-Aware Kernel
Yeongho Kim, Yeonje Choi, Kijung Shin
Text-attributed graphs (TAGs) are widely used in many real-world domains, and learning on TAGs requires jointly modeling text semantics and graph structure. A standard approach for…
cs.LG2026
Effective Dataset Distillation for Spatio-Temporal Forecasting with Bi-dimensional Compression
Taehyung Kwon, Yeonje Choi, Yeongho Kim +1
Spatio-temporal time series are widely used in real-world applications, including traffic prediction and weather forecasting. They are sequences of observations over extensive peri…
physics.soc-ph2025
A Survey on Centrality and Importance Measures in Hypergraphs: Categorization and Empirical Insights
Jaewan Chun, Fanchen Bu, Yeongho Kim +3
Identifying central entities and interactions is a fundamental problem in network science. While well-studied for graphs (pairwise relations), many biological and social systems ex…