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)
Incoming Non-self Citations Over Time
Authors
- 1. Xiaowei Chen (Chinese University of Hong Kong)
- 2. Yongkun Li (University of Science and Technology Beijing)
- 3. Pinghui Wang (Xi'an Jiaotong University)
- 4. John C.S. Lui (Chinese University of Hong Kong)
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}
}
Incoming Citations (Sorted by Pagerank)
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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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