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 (Simon Fraser University)
- 2. Zhefeng Wang (Huawei)
- 3. Jian Pei (Simon Fraser University)
- 4. Yanyan Zhang (Simon Fraser University)
- 5. Yu Yang (Simon Fraser University)
- 6. Enhong Chen (University of Science and Technology Beijing)
BibTeX Citation
@article{chu_vldb19,
title = {{Finding Theme Communities from Database Networks}},
author = {Chu, Lingyang and Wang, Zhefeng and Pei, Jian and Zhang, Yanyan and Yang, Yu and Chen, Enhong},
journal = {PVLDB},
series = {{VLDB} '19},
volume = {12},
number = {10},
pages = {1071--1084},
doi = {10.14778/3339490.3339492},
url = {https://doi.org/10.14778/3339490.3339492},
year = {2019}
}Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
Showing 8 of 8 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 27 | Fast Algorithms for Mining Association Rules | 1994 | VLDB | 0.00052255472 |
| 102 | Truss Decomposition in Massive Networks | 2012 | VLDB | 0.00034255289 |
| 161 | Mining Frequent Patterns without Candidate Generation | 2000 | SIGMOD | 0.00027981772 |
| 189 | Querying K-Truss Community in Large and Dynamic Graphs | 2014 | SIGMOD | 0.00026114928 |
| 364 | Graph Clustering Based on Structural/Attribute Similarities | 2009 | VLDB | 0.00020054172 |
| 1,239 | Attribute-Driven Community Search | 2017 | VLDB | 0.000115381 |
| 2,323 | Truss Decomposition of Probabilistic Graphs: Semantics and Algorithms | 2016 | SIGMOD | 8.750808e-05 |
| 12,158 | ALID: Scalable Dominant Cluster Detection | 2015 | VLDB | 5.093636e-05 |
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