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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
11427
Venue
VLDB
Year
2017
Pagerank
5.8459467e-05
Overall Rank
4,898 | 65.93%
DOI
-

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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
392 Counting Triangles in Data Streams 2006 PODS 0.00024556183
595 Estimating PageRank on Graph Streams 2008 PODS 0.00019507721
1,344 Counting and Sampling Triangles from a Graph Stream 2013 VLDB 0.00012473724
1,472 Space Efficient Mining of Multigraph Streams 2005 PODS 0.00011828662
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