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A General Framework for Estimating Graphlet Statistics via Random Walk

Summary: General framework to estimate graphlet statistics of any size from large graphs via consecutive random-walk samples. Unbiased estimator with Chernoff-Hoeffding sample-size bound and two optimization techniques reduce required samples; experiments show up to an order of magnitude gains in accuracy and time over prior methods. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
11712
Venue
VLDB
Year
2017
Pagerank
9.4093057e-05
Overall Rank
1,958 | 86.57%
DOI
10.14778/3021924.3021940

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{chen_vldb17,
        title = {{A General Framework for Estimating Graphlet Statistics via Random Walk}},
        author = {Chen, Xiaowei and Li, Yongkun and Wang, Pinghui and Lui, John C.S.},
        journal = {PVLDB},
        series = {{VLDB} '17},
        volume = {10},
        number = {3},
        pages = {253--264},
        doi = {10.14778/3021924.3021940},
        url = {https://doi.org/10.14778/3021924.3021940},
        year = {2017}
}

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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
4,497 Leveraging History for Faster Sampling of Online Social Networks 2015 VLDB 6.6618536e-05
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