HyMAGE: Semantic-Aware Dynamic Hypergraph Generation

Abstract

Understanding hypergraph evolution is essential for revealing high-order interaction patterns and generating credible synthetic data when real interaction records are scarce. Existing models suffer from two key limitations: (1) they rely on global topological heuristics that treat nodes as passive entities, yielding poor semantic consistency and generalization; (2) they ignore the influence of node attributes on structural evolution. We propose HyMAGE, a semantic-aware dynamic hypergraph generation framework based on semantic preferential attachment, without any graph-structure pretraining or centralized optimization objective. HyMAGE models each node as an autonomous agent and leverages LLMs for local-level semantic selection, so that hyperedge formation and dissolution emerge from local semantic affinity and structural context in a self-organizing manner. It serves both as a generative model explaining real-world high-order relationship evolution and as a scalable synthetic data factory that distills LLM domain knowledge into explicit hyperedge structures, producing topology-and-semantics-aligned attributed hypergraphs for downstream tasks. Extensive experiments show that HyMAGE significantly outperforms existing methods at both structural and semantic levels. It simultaneously reproduces nine high-order structural patterns of real hypergraphs and generalizes well to downstream tasks: hypergraph neural networks trained solely on HyMAGE-generated data achieve high accuracy, and its diffusion behaviors closely match those of real hypergraphs. These results demonstrate that HyMAGE offers a plausible explanation for high-order evolution mechanisms while providing rich semantic hypergraph training sets for hypergraph learning and mining.