Hypergraph as Language

[2605.21858] Hypergraph as Language Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computation and Language arXiv:2605.21858 (cs) [Submitted on 21 May 2026 ( v1 ), last revised 15 Aug 2026 (this version, v2)] Title: Hypergraph as Language Authors: Mengqi Lei , Guohuan Xie , Shihui Ying , Shaoyi Du , Jun-Hai Yong , Chuan Shi , Ling Tian , Siqi Li , Yue Gao View a PDF of the paper titled Hypergraph as Language, by Mengqi Lei and 8 other authors View PDF HTML (experimental) Abstract: Large language models (LLMs) have recently shown strong potential in modeling relational structures. However, existing approaches remain fundamentally graph-centric: they focus on processing pairwise graph structures into tokens that LLMs can understand. In contrast, many real-world relational patterns do not naturally conform to the pairwise-edge assumption, and are better modeled as high-order associations in hypergraphs. For hypergraph structures, existing methods often fail to preserve the native semantics that multiple objects are jointly connected by the same high-order relation, limiting their ability to exploit complex structures. To address this limitation, we put forth the "Hypergraph as Language" perspective and propose Hyper-Align, a hypergraph-native alignment framework for large language models. Hyper-Align compiles the query-object-centered hypergraph context into hypergraph tokens directly consumable by a base LLM. Specifically, we introduce Hypergraph Incidence Detail Template with Overview (HIDT-O), which serializes high-order association structures into a fixed-shape hybrid template combining local incidence details and overview-level summaries. We then design a Hypergraph Incidence Projector (HIP), which maps native high-order incidence structures into the LLM token space through explicit semantic-structural decoupling and bidirectional message passing between vertices and hyperedges. We further define a concrete Hypergraph-as-Language input protocol, which jointly feeds hypergraph tokens and textual prompts into a frozen base LLM, supporting both vertex-level and hyperedge-level tasks under a unified question-answering paradigm. To systematically evaluate different methods in hypergraph structural modeling, we introduce HyperAlign-Bench. Extensive experiments show that Hyper-Align significantly outperforms existing methods across in-domain and zero-shot evaluations. Subjects: Computation and Language (cs.CL) Cite as: arXiv:2605.21858 [cs.CL] (or arXiv:2605.21858v2 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2605.21858 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Mengqi Lei [ view email ] [v1] Thu, 21 May 2026 01:09:48 UTC (691 KB) [v2] Sat, 15 Aug 2026 13:07:54 UTC (2,033 KB) Full-text links: Access Paper: View a PDF of the paper titled Hypergraph as Language, by Mengqi Lei and 8 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CL < prev | next > new | recent | 2026-05 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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