Scalable Discovery of Best Clusters on Large Graphs
Summary: Top Graph Clusters (TopGC) probabilistically searches large edge-weighted directed graphs for their best clusters in linear time. Parallelizable, supports variable-size overlapping clusters, with a tunable memory parameter; up to 70% speedups and better scores on real-world benchmarks. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Kathy Macropol
- 2. Ambuj Singh
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 353 | Local Search of Communities in Large Graphs | 2014 | SIGMOD | 0.00026277992 |
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Outgoing Citations (Sorted by Pagerank)
Showing 1 of 1 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 34 | Similarity Search in High Dimensions via Hashing | 1999 | VLDB | 0.00076637636 |
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Semantically Similar Papers
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| 2,436 | Efficient Structural Graph Clustering: An Index-Based Approach | 2018 | VLDB | 8.8252104e-05 |
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| 13,251 | Scalable Community Detection via Parallel Correlation Clustering | 2021 | VLDB | - |
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