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Top-K Nearest Keyword Search on Large Graphs

Summary: Reduces exact top-k nearest-keyword search on large weighted graphs to keyword retrieval over distance-oracle shortest-path trees. Provides tree algorithms optimized for small or arbitrarily large k, plus global storage to shrink indexes and accelerate result assembly. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
h10696b4ab6afc67f
Venue
VLDB
Year
2013
Pagerank
5.2928638e-05
Overall Rank
8,662 | 41.77%
DOI
10.14778/2536206.2536210

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{qiao_vldb13,
        title = {{Top-K Nearest Keyword Search on Large Graphs}},
        author = {Qiao, Miao and Qin, Lu and Cheng, Hong and Yu, Jeffrey Xu and Tian, Wentao},
        journal = {PVLDB},
        series = {{VLDB} '13},
        volume = {6},
        number = {10},
        pages = {901--912},
        doi = {10.14778/2536206.2536210},
        url = {https://doi.org/10.14778/2536206.2536210},
        year = {2013}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
7,075 Exact Top-k Nearest Keyword Search in Large Networks 2015 SIGMOD 5.6064033e-05
11,752 TASK: An Efficient Framework for Instant Error-tolerant Spatial Keyword Queries on Road Networks 2023 VLDB 4.9793485e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 10 of 10 cited papers.

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

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