Wedge sampling for computing clustering coefficients and triangle counts on large graphs†

DOI10.1002/sam.11224OpenAlexW2102260665MaRDI QIDQ4969952

Ali Pınar, C. Seshadhri, Tamara G. Kolda

Publication date: 14 October 2020

Published in: Statistical Analysis and Data Mining: The ASA Data Science Journal (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1309.3321

zbMATH Keywords

directed trianglestriangle countingclustering coefficientstriangle characteristicswedge sampling

Mathematics Subject Classification ID

Statistics (62-XX) Computer science (68-XX)

Related Items (6)

On the efficacy of higher-order spectral clustering under weighted stochastic block models ⋮ Estimating the clustering coefficient using sample complexity analysis ⋮ On the Complexity of Sampling Vertices Uniformly from a Graph ⋮ Decompositions of Triangle-Dense Graphs ⋮ Trigger Detection for Adaptive Scientific Workflows Using Percentile Sampling ⋮ A Fast Counting Method for 6-Motifs with Low Connectivity

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