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
- 2. Jieming Shi
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 13,099 | 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 |
|---|---|---|---|---|
| 33 | BIRCH: An Efficient Data Clustering Method for Very Large Databases | 1996 | SIGMOD | 0.00077399244 |
| 1,528 | Homogeneous Network Embedding for Massive Graphs via Reweighted Personalized PageRank | 2020 | VLDB | 0.00011489661 |
| 5,757 | Scalable and Effective Bipartite Network Embedding | 2022 | SIGMOD | 5.3386415e-05 |
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