Counting Triangles in Massive Graphs with MapReduce

DOI10.1137/13090729XzbMath1306.05237arXiv1301.5887MaRDI QIDQ2940042

Todd Plantenga, Christine Task, Ali Pınar, C. Seshadhri, Tamara G. Kolda

Publication date: 23 January 2015

Published in: SIAM Journal on Scientific Computing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1301.5887

zbMATH Keywords

clustering coefficientMapReducetriangle countinglarge-scale networkstriangle characteristics

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Graph algorithms (graph-theoretic aspects) (05C85) Randomized algorithms (68W20) Distributed algorithms (68W15) Numerical algorithms for specific classes of architectures (65Y10)

Related Items (5)

A Hierarchy of Network Models Giving Bistability Under Triadic Closure ⋮ Distributed discovery of frequent subgraphs of a network using MapReduce ⋮ Approximately Counting Triangles in Sublinear Time ⋮ Estimating the clustering coefficient using sample complexity analysis ⋮ Trigger Detection for Adaptive Scientific Workflows Using Percentile Sampling

Uses Software

• MapReduce
• GraphLab
• UbiCrawler

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