Hypergraph Representation Learning with Hyperlink Random Effects
Hypergraphs record multi-way interactions among entities. Extracting information from the combinatorial structure underlying observed multi-way interactions is a central task in many real-world problems. Existing methods face several limitations. First, many deep architectures for hypergraphs do not explicitly exploit the potential low-rank structure, which can sacrifice parsimony and interpretability in the learned representations. Second, many low-rank-based methods operate on tensor representations, which typically require hyperlinks to have uniform sizes and thus limit their applicability to general hypergraphs with non-uniform hyperlink sizes. Third, many methods ignore the fact that...
| Authors | Zimeng Li, Shihao Wu, Gongjun Xu et al. |
|---|---|
| Primary category | stat.ML |
| Published | 3 October 2026 |
| Last updated at source | 3 October 2026 |
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