Efficient High-Quality Clustering for Large Bipartite Graphs
Summary: Efficient k-BGC on large bipartite graphs with HOPE/HOPE+, using HOP vectors and low-rank approximations for quality clustering. HOPE+ (FNEM/SNEM) achieves top accuracy with two-stage optimization, scaling to 1.1B edges in ~31 minutes. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Renchi Yang (Hong Kong Baptist University)
- 2. Jieming Shi (Hong Kong Polytechnic University)
BibTeX Citation
@inproceedings{yang_sigmod24,
title = {{Efficient High-Quality Clustering for Large Bipartite Graphs}},
author = {Yang, Renchi and Shi, Jieming},
series = {{SIGMOD} '24},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3639278},
url = {https://dl.acm.org/doi/10.1145/3639278},
year = {2024}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 13,289 | Effective Clustering for Large Multi-Relational Graphs | 2026 | SIGMOD | - |
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Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 31 | BIRCH: An Efficient Data Clustering Method for Very Large Databases | 1996 | SIGMOD | 0.00050347119 |
| 1,968 | Homogeneous Network Embedding for Massive Graphs via Reweighted Personalized PageRank | 2020 | VLDB | 9.3752727e-05 |
| 5,432 | Scalable and Effective Bipartite Network Embedding | 2022 | SIGMOD | 6.2211042e-05 |
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