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An Optimal Algorithm for Triangle Counting in the Stream

Authors Rajesh Jayaram, John Kallaugher

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Author Details

Rajesh Jayaram
  • Carnegie Mellon University, Pittsburgh, PA, USA
John Kallaugher
  • The University of Texas at Austin, Austin, TX, USA

Funding

  • Jayaram, Rajesh: Rajesh Jayaram would like to acknowledge partial support from the Office of Naval Research (ONR) under grant Number N00014-18-1-2562, and the National Science Foundation (NSF) under Grant Number CCF-1815840.
  • Kallaugher, John: John Kallaugher would like to acknowledge support from the National Science Foundation (NSF) under Grant Number CCF-1751040 (CAREER).

Cite AsGet BibTex

Rajesh Jayaram and John Kallaugher. An Optimal Algorithm for Triangle Counting in the Stream. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 207, pp. 11:1-11:11, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)
https://doi.org/10.4230/LIPIcs.APPROX/RANDOM.2021.11

Abstract

We present a new algorithm for approximating the number of triangles in a graph G whose edges arrive as an arbitrary order stream. If m is the number of edges in G, T the number of triangles, Δ_E the maximum number of triangles which share a single edge, and Δ_V the maximum number of triangles which share a single vertex, then our algorithm requires space: Õ(m/T⋅(Δ_E + √{Δ_V})) Taken with the Ω((m Δ_E)/T) lower bound of Braverman, Ostrovsky, and Vilenchik (ICALP 2013), and the Ω((m √{Δ_V})/T) lower bound of Kallaugher and Price (SODA 2017), our algorithm is optimal up to log factors, resolving the complexity of a classic problem in graph streaming.

Subject Classification

ACM Subject Classification
  • Theory of computation → Sketching and sampling
Keywords
  • Triangle Counting
  • Streaming
  • Graph Algorithms
  • Sampling
  • Sketching

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