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Better Algorithms for Counting Triangles in Data Streams

Summary: Tightens space for 1+ε triangle-count approximation in streams: adjacency-list model achieves O~(ε^{-2} m / sqrt(T)) in one pass and O~(ε^{-2} m^{3/2} / T) in two passes; arbitrary-order achieves O~(ε^{-2} m / T) in two passes and O~(ε^{-2} m^{3/2} / T) in three passes (with degree oracle). Introduces an efficient wedge-sampling implementation via the first multi-sample l_p sampling algorithm with O(polylog n) update time, a broadly applicable primitive. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
1678
Venue
PODS
Year
2016
Pagerank
5.7405307e-05
Overall Rank
5,046 | 64.90%
DOI
10.1145/2902251.2902283

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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
392 Counting Triangles in Data Streams 2006 PODS 0.00024556183
1,040 Graph Sketches: Sparsification, Spanners, and Subgraphs 2012 PODS 0.00014488943
1,094 Tight Bounds for Lp Samplers, Finding Duplicates in Streams, and Related Problems 2011 PODS 0.00014129658
1,344 Counting and Sampling Triangles from a Graph Stream 2013 VLDB 0.00012473724
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