用范畴论分析超边解耦，提升模型发现隐含关系的能力
Disentangling Hyperedges through the Lens of Category Theory
- 从范畴论出发，提出基于自然性条件的超边解耦准则
- 实验验证能有效捕捉基因通路中的功能关联关系
- 适合研究复杂关系建模与生物网络分析的读者
尽管解耦表示学习在发现图结构数据中的潜在模式方面表现优异，但针对超图结构数据的解耦研究仍较少。将超边解耦引入超图神经网络，使模型能够利用与标签相关的隐藏超边语义，如节点间的未标注关系。本文从范畴论视角分析超边解耦，并提出一种基于自然性条件的新解耦准则。概念验证模型实验表明，该准则在捕捉基因通路中基因（节点）的功能关系方面具有潜力。
原文摘要 · Abstract (English)
Despite the promising results of disentangled representation learning in discovering latent patterns in graph-structured data, few studies have explored disentanglement for hypergraph-structured data. Integrating hyperedge disentanglement into hypergraph neural networks enables models to leverage hidden hyperedge semantics, such as unannotated relations between nodes, that are associated with labels. This paper presents an analysis of hyperedge disentanglement from a category-theoretical perspective and proposes a novel criterion for disentanglement derived from the naturality condition. Our proof-of-concept model experimentally showed the potential of the proposed criterion by successfully capturing functional relations of genes (nodes) in genetic pathways (hyperedges).
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