Author

D. Y. Kang

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Book Open access Aug 2026

HyperGC: Learning Hypergraph Representations via Full Hyperedge Reconstruction and Contrastive Evaluation

The proposed HyperGC is a hybrid self-supervised HRL framework that unifies generative and contrastive learning through two core strategies: (S1) full hyperedge reconstruction via progressive selection with dynamic target cross-entropy, and (S2) multi-view contrastive evaluation without handcrafted negative samples.

D. Y. Kang, So-Bin Jung, Sang-Wook Kim · Proceedings of the 32nd ACM... · 0 citations

Preprint Aug 2026

TAHB: A Comprehensive Benchmark for Text-Attributed Hypergraph Learning

TAHB (Text-Attributed Hypergraph Benchmark) is presented, the first public benchmark integrating hypergraph structures and raw textual attributes, and shows that LLM-enhanced textual semantics improve hypergraph learning performance, while structural and textual information jointly provide the best setting for LLM-base...

D. Y. Kang, Junghyun Kim, Ju-hyun Jeon et al. · 0 citations

#machine learning Preprint Aug 2026

Who Should Teach? Confidence-Aware Dual-Teacher Learning for Few-Shot Node Classification on Text-Attributed Graphs

This work proposes CoTeach, a Confidence-aware dual-teacher learning framework that dynamically selects the more reliable teacher for each node, and demonstrates that CoTeach consistently improves few-shot node classification performance while reducing unnecessary LLM utilization and associated monetary costs.

Hojin Kim, Sujin Yoon, Sung-Su Lim et al. · 0 citations