Effective and Efficient Attributed Hypergraph Embedding on Nodes and Hyperedges
Summary: SAHE jointly embeds nodes and hyperedges by preserving two higher-order similarities (HMS-N, HMS-E) on an extended hypergraph that incorporates attribute-based hyperedges. It unifies approximations and avoids dense-matrix materialization to scale efficiently, improving embedding quality and runtime versus 11 baselines. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Yiran Li
- 2. Gongyao Guo
- 3. Chen Feng
- 4. Jieming Shi
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,474 | Homogeneous Network Embedding for Massive Graphs via Reweighted Personalized PageRank | 2020 | VLDB | 0.00011825229 |
| 3,503 | FREDE: Anytime Graph Embeddings | 2021 | VLDB | 7.0355661e-05 |
| 3,803 | Scaling Attributed Network Embedding to Massive Graphs | 2021 | VLDB | 6.7550628e-05 |
| 5,110 | LightNE: A Lightweight Graph Processing System for Network Embedding | 2021 | SIGMOD | 5.6901951e-05 |
| 5,827 | On Graph Representation for Attributed Hypergraph Clustering | 2025 | SIGMOD | 5.3113542e-05 |
| 7,850 | Efficient and Effective Attributed Hypergraph Clustering via K-Nearest Neighbor Augmentation | 2023 | SIGMOD | 4.6362484e-05 |
| 7,933 | Billion-Scale Bipartite Graph Embedding: A Global-Local Induced Approach | 2024 | VLDB | 4.613363e-05 |
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