56 citations · 71 across the 20 of their papers we have counts for
4 papers · 2 filters
Do Higher-Order Models Win for Higher-Order Reasons? Rethinking Performance Gains in Hypergraph Learning
Fanchen Bu, Fan Li, Geon Lee +4
Higher-order models (e.g., hypergraph neural networks) often outperform lower-order baselines on hypergraph learning benchmarks, and their advantages are commonly attributed to the…
Message Passing Does More with Less for In-Context Learning on Graphs
Dooho Lee, Jinmo Lee, Minho Jeong +2
Achieving strong performance with graph neural networks (GNNs) typically requires training and hyperparameter tuning for each dataset, incurring repeated costs and effort. Graph in…
SLeDGe: Semi-Supervised Learning on Data Streams with Graph Structure Learning
Heechan Moon, Kijung Shin
Semi-supervised learning (SSL) on data streams is challenging due to the continuous evolution of high-volume data and the scarcity of labels. Existing methods are limited in levera…
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…