# Structural Deep Embedding for Hyper-Networks

arXiv: https://arxiv.org/abs/1711.10146 · HTML: https://mlsanity.com/paper/1711.10146/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2017.

## Built on by (most cited first, 30 of 33)

- [Signal Processing on Higher-Order Networks: Livin' on the Edge ... and Beyond](https://mlsanity.com/paper/2101.05510/) (2021)
- [Double-Scale Self-Supervised Hypergraph Learning for Group Recommendation](https://mlsanity.com/paper/2109.04200/) (2021)
- [Learnable Hypergraph Laplacian for Hypergraph Learning](https://mlsanity.com/paper/2106.05701/) (2021)
- [Multiplex Bipartite Network Embedding using Dual Hypergraph Convolutional Networks](https://mlsanity.com/paper/2102.06371/) (2021)
- [AHP: Learning to Negative Sample for Hyperedge Prediction](https://mlsanity.com/paper/2204.06353/) (2022)
- [Road Network Representation Learning: A Dual Graph based Approach](https://mlsanity.com/paper/2304.07298/) (2023)
- [Hypergraph Pre-training with Graph Neural Networks](https://mlsanity.com/paper/2105.10862/) (2021)
- [A Survey on Hyperlink Prediction](https://mlsanity.com/paper/2207.02911/) (2022)
- [A Survey on Visual Transfer Learning using Knowledge Graphs](https://mlsanity.com/paper/2201.11794/) (2022)
- [R5: Rule Discovery with Reinforced and Recurrent Relational Reasoning](https://mlsanity.com/paper/2205.06454/) (2022)
- [SHINE: SubHypergraph Inductive Neural nEtwork](https://mlsanity.com/paper/2210.07309/) (2022)
- [A Community Detection and Graph Neural Network Based Link Prediction Approach for Scientific Literature](https://mlsanity.com/paper/2401.02542/) (2024)
- [Graph Learning and Its Advancements on Large Language Models: A Holistic Survey](https://mlsanity.com/paper/2212.08966/) (2022)
- [Demystifying Higher-Order Graph Neural Networks](https://mlsanity.com/paper/2406.12841/) (2024)
- [HGCN(O): A Self-Tuning GCN HyperModel Toolkit for Outcome Prediction in Event-Sequence Data](https://mlsanity.com/paper/2507.22524/) (2025)
- [Unsupervised Deep Manifold Attributed Graph Embedding](https://mlsanity.com/paper/2104.13048/) (2021)
- [HyperTeNet: Hypergraph and Transformer-based Neural Network for Personalized List Continuation](https://mlsanity.com/paper/2110.01467/) (2021)
- [Abnormal Event Detection via Hypergraph Contrastive Learning](https://mlsanity.com/paper/2304.01226/) (2023)
- [ResVGAE: Going Deeper with Residual Modules for Link Prediction](https://mlsanity.com/paper/2105.00695/) (2021)
- [VER: Unifying Verbalizing Entities and Relations](https://mlsanity.com/paper/2211.11093/) (2022)
- [Zoo Guide to Network Embedding](https://mlsanity.com/paper/2305.03474/) (2023)
- [Enhancing Hyperedge Prediction with Context-Aware Self-Supervised Learning](https://mlsanity.com/paper/2309.05798/) (2023)
- [HeteroMILE: a Multi-Level Graph Representation Learning Framework for Heterogeneous Graphs](https://mlsanity.com/paper/2404.00816/) (2024)
- [A Framework for Joint Unsupervised Learning of Cluster-Aware Embedding for Heterogeneous Networks](https://mlsanity.com/paper/2108.03953/) (2021)
- [The CAT SET on the MAT: Cross Attention for Set Matching in Bipartite Hypergraphs](https://mlsanity.com/paper/2111.00243/) (2021)
- [Exploring Representation of Horn Clauses using GNNs (Extended Technical Report)](https://mlsanity.com/paper/2206.06986/) (2022)
- [Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods](https://mlsanity.com/paper/2506.05626/) (2025)
- [Scalable Hypergraph Embedding System](https://mlsanity.com/paper/2103.09660/) (2021)
- [Learnable Hypergraph Laplacian for Hypergraph Learning](https://mlsanity.com/paper/2106.06666/) (2021)
- [HyperS2V: A Framework for Structural Representation of Nodes in Hyper Networks](https://mlsanity.com/paper/2311.04149/) (2023)

## Abstract (arXiv)

Network embedding has recently attracted lots of attentions in data mining. Existing network embedding methods mainly focus on networks with pairwise relationships. In real world, however, the relationships among data points could go beyond pairwise, i.e., three or more objects are involved in each relationship represented by a hyperedge, thus forming hyper-networks. These hyper-networks pose great challenges to existing network embedding methods when the hyperedges are indecomposable, that is to say, any subset of nodes in a hyperedge cannot form another hyperedge. These indecomposable hyperedges are especially common in heterogeneous networks. In this paper, we propose a novel Deep Hyper-Network Embedding (DHNE) model to embed hyper-networks with indecomposable hyperedges. More specifically, we theoretically prove that any linear similarity metric in embedding space commonly used in existing methods cannot maintain the indecomposibility property in hyper-networks, and thus propose a new deep model to realize a non-linear tuplewise similarity function while preserving both local and global proximities in the formed embedding space. We conduct extensive experiments on four different types of hyper-networks, including a GPS network, an online social network, a drug network and a semantic network. The empirical results demonstrate that our method can significantly and consistently outperform the state-of-the-art algorithms.