5 papers
Who Should Teach? Confidence-Aware Dual-Teacher Learning for Few-Shot Node Classification on Text-Attributed Graphs
Hojin Kim, Sujin Yoon, Sungsu Lim +2
Text-Attributed Graphs (TAGs) integrate graph structures and node-associated textual attributes, and recent studies have increasingly leveraged Large Language Models (LLMs) to impr…
TAHB: A Comprehensive Benchmark for Text-Attributed Hypergraph Learning
David Yoon Suk Kang, JungHyun Kim, Juhyun Jeon +1
Hypergraphs effectively model higher-order groupwise relationships beyond pairwise interactions, while pretrained language models (PLMs) and large language models (LLMs) provide ri…
Heterophily-Aware Adaptive Knowledge Distillation for Hypergraph Neural Networks
Joohee Cho, David Yoon Suk Kang, Yunyong Ko
Hypergraph knowledge distillation aims to retain the predictive performance of a hypergraph neural network (HNN) teacher while reducing inference costs through a lightweight studen…
Anchored Alignment: Preventing Positional Collapse in Multimodal Recommender Systems
Yonghun Jeong, David Yoon Suk Kang, Yeon-Chang Lee
Multimodal recommender systems (MMRS) leverage images, text, and interaction signals to enrich item representations. However, recent alignment based MMRSs that enforce a unified em…
Improving the Accuracy of Community Detection on Signed Networks via Community Refinement and Contrastive Learning
Hyunuk Shin, Hojin Kim, Chanyoung Lee +2
Community detection (CD) on signed networks is crucial for understanding how positive and negative relations jointly shape network structure. However, existing CD methods often yie…