Finding Theme Communities from Database Networks
Summary: Theme communities are cohesive subgraphs in database networks where a common pattern is frequent across vertex-associated databases. To tackle #P-hard counting, TCFI prunes infeasible patterns and TC-Tree indexes and enables sub-second retrieval of hundreds of millions of theme communities, as shown by extensive experiments and a case study. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Lingyang Chu
- 2. Zhefeng Wang
- 3. Jian Pei
- 4. Yanyan Zhang
- 5. Yu Yang
- 6. Enhong Chen
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 36 | Fast Algorithms for Mining Association Rules | 1994 | VLDB | 0.00076161096 |
| 108 | Truss Decomposition in Massive Networks | 2012 | VLDB | 0.00048300163 |
| 181 | Mining Frequent Patterns without Candidate Generation | 2000 | SIGMOD | 0.00036992674 |
| 283 | Querying K-Truss Community in Large and Dynamic Graphs | 2014 | SIGMOD | 0.00029041257 |
| 313 | Graph Clustering Based on Structural/Attribute Similarities | 2009 | VLDB | 0.00028097557 |
| 1,645 | Attribute-Driven Community Search | 2017 | VLDB | 0.00011037459 |
| 2,684 | Truss Decomposition of Probabilistic Graphs: Semantics and Algorithms | 2016 | SIGMOD | 8.3136866e-05 |
| 11,960 | ALID: Scalable Dominant Cluster Detection | 2015 | VLDB | 4.1945683e-05 |
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