TAHB: A Comprehensive Benchmark for Text-Attributed Hypergraph Learning
Hypergraphs effectively model higher-order groupwise relationships beyond pairwise interactions, while pretrained language models (PLMs) and large language models (LLMs) provide rich semantic understanding from textual attributes. However, research on combining language models with hypergraph learning remains limited due to the lack of public text-attributed hypergraph benchmarks. To address this limitation, we present TAHB (Text-Attributed Hypergraph Benchmark), the first public benchmark integrating hypergraph structures and raw textual attributes. TAHB contains 10 real-world datasets from four domains - e-commerce, academia, movies, and politics networks - enabling systematic evaluatio...
| Authors | David Yoon Suk Kang, JungHyun Kim, Juhyun Jeon et al. |
|---|---|
| Primary category | cs.AI |
| Published | 15 August 2026 |
| Last updated at source | 15 August 2026 |
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