DBScholar

Back to papers

On Sampling from Massive Graph Streams

Summary: GPS is an order-based reservoir sampling framework for graphs, weighting edge samples to optimize subgraph estimation. Separates sampling from estimation (post- and in-stream) with a Martingale-based unbiased estimator; yields <1% error on subgraph counts while using <0.01% of edges. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
h3db374dc8111b387
Venue
VLDB
Year
2017
Pagerank
6.5256176e-05
Overall Rank
4,575 | 69.25%
DOI
10.14778/3137628.3137652

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{ahmed_vldb17,
        title = {{On Sampling from Massive Graph Streams}},
        author = {Ahmed, Nesreen K. and Duffield, Nick and Willke, Theodore L. and Rossi, Ryan A.},
        journal = {PVLDB},
        series = {{VLDB} '17},
        volume = {10},
        number = {11},
        pages = {1430--1443},
        doi = {10.14778/3137628.3137652},
        url = {https://doi.org/10.14778/3137628.3137652},
        year = {2017}
}

Incoming Citations (Sorted by Pagerank)

Showing 5 of 5 citing papers.

Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 4 of 4 cited papers.

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

Rank Cited Paper Year Venue Pagerank
462 Counting Triangles in Data Streams 2006 PODS 0.00017807125
1,121 Counting and Sampling Triangles from a Graph Stream 2013 VLDB 0.000119396
1,415 Estimating PageRank on Graph Streams 2008 PODS 0.00010737378
2,054 Space Efficient Mining of Multigraph Streams 2005 PODS 9.1135465e-05
Previous Page 1 / 1 Next

Semantically Similar Papers