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Fast Approximate Counting of Cycles

Authors Keren Censor-Hillel , Tomer Even, Virginia Vassilevska Williams

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Subject Classification

ACM Subject Classification
  • Mathematics of computing → Approximation algorithms
  • Mathematics of computing → Graph algorithms
Keywords
  • Approximate triangle counting
  • Approximate cycle counting Fast matrix multiplication
  • Fast rectangular matrix multiplication

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Abstract

We consider the problem of approximate counting of triangles and longer fixed length cycles in directed graphs. For triangles, Tětek [ICALP'22] gave an algorithm that returns a (1±ε)-approximation in Õ(n^ω/t^{ω-2}) time, where t is the unknown number of triangles in the given n node graph and ω < 2.372 is the matrix multiplication exponent. We obtain an improved algorithm whose running time is, within polylogarithmic factors the same as that for multiplying an n× n/t matrix by an n/t × n matrix. We then extend our framework to obtain the first nontrivial (1± ε)-approximation algorithms for the number of h-cycles in a graph, for any constant h ≥ 3. Our running time is Õ(MM(n,n/t^{1/(h-2)},n)), the time to multiply n × n/(t^{1/(h-2)}) by n/(t^{1/(h-2)) × n matrices.
Finally, we show that under popular fine-grained hypotheses, this running time is optimal.

Cite As Get BibTex

Keren Censor-Hillel, Tomer Even, and Virginia Vassilevska Williams. Fast Approximate Counting of Cycles. In 51st International Colloquium on Automata, Languages, and Programming (ICALP 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 297, pp. 37:1-37:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024) https://doi.org/10.4230/LIPIcs.ICALP.2024.37

Author Details

Keren Censor-Hillel
  • Department of Computer Science, Technion, Haifa, Israel
Tomer Even
  • Department of Computer Science, Technion, Haifa, Israel
Virginia Vassilevska Williams
  • Massachusetts Institute of Technology, Cambridge, MA, USA

Funding

  • Censor-Hillel, Keren: The research is supported in part by the Israel Science Foundation (grant 529/23).
  • Vassilevska Williams, Virginia: Supported by NSF Grant CCF-2330048, BSF Grant 2020356, and a Simons Investigator Award.

Acknowledgements

We would like to thank the anonymous reviewers for their invaluable feedback and for identifying a technical issue in a previous version of our paper.

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