Efficient and Effective Attributed Hypergraph Clustering via K-Nearest Neighbor Augmentation
Summary: AHCKA employs KNN augmentation to leverage node attributes in hypergraphs for improved attributed hypergraph clustering. A joint hypergraph random-walk objective plus a fast solver yields state-of-the-art results with massive speedups on real data. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Yiran Li (Hong Kong Polytechnic University)
- 2. Renchi Yang (Hong Kong Baptist University)
- 3. Jieming Shi (Hong Kong Polytechnic University)
BibTeX Citation
@inproceedings{li_sigmod23,
title = {{Efficient and Effective Attributed Hypergraph Clustering via K-Nearest Neighbor Augmentation}},
author = {Li, Yiran and Yang, Renchi and Shi, Jieming},
series = {{SIGMOD} '23},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3589261},
url = {https://dl.acm.org/doi/10.1145/3589261},
year = {2023}
}
Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,960 | On Graph Representation for Attributed Hypergraph Clustering | 2025 | SIGMOD | 6.0274692e-05 |
| 10,973 | Effective and Efficient Attributed Hypergraph Embedding on Nodes and Hyperedges | 2025 | VLDB | 5.093636e-05 |
| 13,289 | Effective Clustering for Large Multi-Relational Graphs | 2026 | SIGMOD | - |
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Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 364 | Graph Clustering Based on Structural/Attribute Similarities | 2009 | VLDB | 0.00020054172 |
| 2,073 | BePI: Fast and Memory-Efficient Method for Billion-Scale Random Walk with Restart | 2017 | SIGMOD | 9.2209912e-05 |
| 2,644 | A Model-based Approach to Attributed Graph Clustering | 2012 | SIGMOD | 8.3043854e-05 |
| 5,503 | MEGA: Multi-View Semi-Supervised Clustering of Hypergraphs | 2020 | VLDB | 6.1948574e-05 |
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