基于强度驱动机制生成高阶关系图，提升可解释性与多样性。
HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings
- 通过潜变量强度控制节点与超边的连接关系，实现可解释的生成机制。
- 在真实与合成数据上均实现高保真度，且保持强新颖性与多样性。
- 适合研究复杂网络建模、推荐系统及分子结构生成的学者使用。
超图提供了建模多体交互的合理框架，广泛应用于推荐系统、社交网络和分子建模。由于关联结构是离散、稀疏且受异质高阶交互制约，超图生成仍具挑战性。现有方法常依赖隐式潜在空间或连续关联解码器，难以解释节点-超边关联的形成机制。为此，我们提出HYVINT，一种强度驱动的超图生成框架。核心创新有二：(i) 设计了基于强度的关联形成机制，将潜交互强度与二值关联相联系；(ii) 推导出可计算的变分下界估计器以学习潜表示。我们给出了生成误差界及渐近收敛速率，并在合成与真实超图上实证表明，HYVINT在保持高保真度的同时具备显著的新颖性与多样性。
原文摘要 · Abstract (English)
Hypergraphs provide a principled framework for modeling polyadic interactions, with applications in recommendation systems, social networks, and molecular modeling. Hypergraph generation remains challenging because incidence structures are discrete, sparse, and governed by heterogeneous higher-order interactions. Existing generators often rely on implicit latent spaces or continuous incidence decoders, which provide limited mechanistic interpretation of how node-hyperedge incidences arise. To address these limitations, we propose HYVINT, an intensity-driven hypergraph generative framework. Our key innovations are twofold: (i) we develop an intensity-driven incidence formation mechanism for hypergraphs that links latent interaction strength to binary incidence, and (ii) we derive a tractable lower-bound variational estimator for learning latent representations. We provide generation error bounds with asymptotic convergence rates and empirically show that HYVINT achieves strong fidelity while maintaining substantial novelty and diversity on synthetic and real-world hypergraphs.
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