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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
11525
Venue
VLDB
Year
2017
Pagerank
0.00010736699
Overall Rank
1,724 | 88.02%
DOI
-

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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
2,102 Leveraging History for Faster Sampling of Online Social Networks 2015 VLDB 9.5379264e-05
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