Hypergraph as Language

2026-05-20

AI Summary

Existing large language models struggle to effectively capture the high-order relational semantics inherent in hypergraphs due to their reliance on pairwise graph representations. To address this limitation, this work introduces a novel “hypergraph-as-language” paradigm and proposes Hyper-Align, a native hypergraph alignment framework that compiles hypergraph context into token sequences comprehensible to large language models through semantic-structural disentanglement and bidirectional message passing. Hyper-Align pioneers a linguistically grounded hypergraph representation by integrating a Hypergraph Incidence Detail Template (HIDT-O), a Hypergraph Incidence Projector (HIP), and a unified input protocol, enabling both vertex- and hyperedge-level question answering with frozen large language models. Evaluated on the newly curated HyperAlign-Bench benchmark, Hyper-Align significantly outperforms existing methods under both in-domain and zero-shot settings.

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.

Problem

• hypergraph
• large language models
• high-order relations
• relational structures
• graph-centric limitation

Innovation

• hypergraph
• large language models
• high-order relations
• semantic-structural decoupling
• hypergraph alignment