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Top-K Structural Diversity Search in Large Networks

Summary: Introduces the first top-k structural-diversity search for large networks, ranking users by connected components in their neighborhoods. An incrementally refined upper bound enables pruning, while heuristic evaluation strategies improve scalability across 13 real-world networks. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
10838
Venue
VLDB
Year
2013
Pagerank
5.7055799e-05
Overall Rank
7,094 | 51.33%
DOI
10.14778/2536258.2536272

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{huang_vldb13,
        title = {{Top-K Structural Diversity Search in Large Networks}},
        author = {Huang, Xin and Cheng, Hong and Li, Rong-Hua and Qin, Lu and Yu, Jeffrey Xu},
        journal = {PVLDB},
        series = {{VLDB} '13},
        volume = {6},
        number = {13},
        pages = {1618--1629},
        doi = {10.14778/2536258.2536272},
        url = {https://doi.org/10.14778/2536258.2536272},
        year = {2013}
}

Incoming Citations (Sorted by Pagerank)

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Outgoing Citations (Sorted by Pagerank)

Showing 6 of 6 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
5 Optimal Aggregation Algorithms for Middleware [Extended Abstract] 2001 PODS 0.0010828372
102 Truss Decomposition in Massive Networks 2012 VLDB 0.00034255289
827 Minimal Probing: Supporting Expensive Predicates for Top-k Queries 2002 SIGMOD 0.00013769938
930 SPARK: Top-k Keyword Query in Relational Databases 2007 SIGMOD 0.0001312728
1,153 Efficient Diversity-Aware Search 2011 SIGMOD 0.0001193423
1,454 Diversifying Top-K Results 2012 VLDB 0.00010739504
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