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gSketch: On Query Estimation in Graph Streams

Summary: gSketch fuses traditional stream synopses with partitioned sketches to estimate queries on evolving graphs. It splits a global sketch into localized sketches to optimize accuracy under two scenarios: stream-only sampling, and joint stream+workload sampling, outperforming global baselines on real and synthetic data. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
10442
Venue
VLDB
Year
2012
Pagerank
8.8181328e-05
Overall Rank
2,439 | 83.06%
DOI
-

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Showing 8 of 8 cited papers.

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

Rank Cited Paper Year Venue Pagerank
168 Approximate Frequency Counts over Data Streams 2002 VLDB 0.0003915627
389 Counting Triangles in Data Streams 2006 PODS 0.00024649634
589 Estimating PageRank on Graph Streams 2008 PODS 0.00019569121
831 Finding Frequent Items in Data Streams 2008 VLDB 0.00016094846
1,065 Processing Complex Aggregate Queries over Data Streams 2002 SIGMOD 0.00014344675
1,466 Space Efficient Mining of Multigraph Streams 2005 PODS 0.00011838607
3,930 Tighter Estimation using Bottom-k Sketches 2008 VLDB 6.6195837e-05
4,092 On Dense Pattern Mining in Graph Streams [Extended Abstract] 2010 VLDB 6.4525563e-05
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