Efficient and Effective Algorithms for Clustering Uncertain Graphs
Summary: Clustering graphs with edge uncertainty under k-median and k-center objectives, maximizing probability to cluster centers. Gives (1-1/e)-approx for k-median and OPT_ck-approx to k-center; practical optimizations; experiments show speed and quality gains. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Kai Han
- 2. Fei Gui
- 3. Xiaokui Xiao
- 4. Jing Tang
- 5. Yuntian He
- 6. Zongmai Cao
- 7. He Huang
Incoming Citations (Sorted by Pagerank)
Showing 6 of 6 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,158 | Butterfly Counting on Uncertain Bipartite Graphs | 2022 | VLDB | 6.3898204e-05 |
| 5,468 | MEGA: Multi-View Semi-Supervised Clustering of Hypergraphs | 2020 | VLDB | 5.4896448e-05 |
| 6,164 | Shortest Paths and Centrality in Uncertain Networks | 2021 | VLDB | 5.1731827e-05 |
| 8,909 | Shortest Paths Discovery in Uncertain Networks via Transfer Learning | 2023 | SIGMOD | 4.4229886e-05 |
| 9,135 | Sage: A System for Uncertain Network Analysis | 2022 | VLDB | 4.3846727e-05 |
| 11,261 | Scaling Up Structural Clustering to Large Probabilistic Graphs Using Lyapunov Central Limit Theorem | 2023 | VLDB | 4.1905499e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 14 of 14 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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