skip to main content
10.1145/3294052.3319706acmconferencesArticle/Chapter ViewAbstractPublication PagespodsConference Proceedingsconference-collections
research-article

The Complexity of Counting Cycles in the Adjacency List Streaming Model

Published:25 June 2019Publication History

ABSTRACT

We study the problem of counting cycles in the adjacency list streaming model, fully resolving in which settings there exist sublinear space algorithms. Our main upper bound is a two-pass algorithm for estimating triangles that uses $\wtO (m/T^2/3 )$ space, where m is the edge count and T is the triangle count of the graph. On the other hand, we show that no sublinear space multipass algorithm exists for counting $\ell$-cycles for $\ell \geq 5$. Finally, we show that counting 4-cycles is intermediate: sublinear space algorithms exist in multipass but not single-pass settings.

References

  1. Lars Arge, Michael T. Goodrich, and Nodari Sitchinava. 2010. Parallel external memory graph algorithms. In 24th IEEE International Symposium on Parallel and Distributed Processing, IPDPS 2010, Atlanta, Georgia, USA, 19--23 April 2010 - Conference Proceedings. 1--11.Google ScholarGoogle ScholarCross RefCross Ref
  2. Shaikh Arifuzzaman, Maleq Khan, and Madhav V. Marathe. 2013. PATRIC: a parallel algorithm for counting triangles in massive networks. In 22nd ACMInternational Conference on Information and Knowledge Management, CIKM'13, San Francisco, CA, USA, October 27 - November 1, 2013. 529--538. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Ziv Bar-Yossef, Ravi Kumar, and D. Sivakumar. 2002. Reductions in streaming algorithms, with an application to counting triangles in graphs. In Proceedings of the Thirteenth Annual ACM-SIAM Symposium on Discrete Algorithms, January 6--8, 2002, San Francisco, CA, USA. 623-- 632. http://dl.acm.org/citation.cfm?id=545381.545464 Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Luca Becchetti, Paolo Boldi, Carlos Castillo, and Aristides Gionis. 2008. Efficient Semi-streaming Algorithms for Local Triangle Counting in Massive Graphs. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '08). ACM, New York, NY, USA, 16--24. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Suman K. Bera and Amit Chakrabarti. 2017. Towards Tighter Space Bounds for Counting Triangles and Other Substructures in Graph Streams. In 34th Symposium on Theoretical Aspects of Computer Science (STACS 2017) (Leibniz International Proceedings in Informatics (LIPIcs)), Heribert Vollmer and Brigitte Vallée (Eds.), Vol. 66. Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik, Dagstuhl, Germany, 11:1-- 11:14.Google ScholarGoogle Scholar
  6. Jonathan W. Berry, Bruce Hendrickson, Simon Kahan, and Petr Konecny. 2007. Software and Algorithms for Graph Queries on Multithreaded Architectures. In 21th International Parallel and Distributed Processing Symposium (IPDPS 2007), Proceedings, 26--30 March 2007, Long Beach, California, USA. 1--14.Google ScholarGoogle Scholar
  7. Jonathan W. Berry, Bruce Hendrickson, Randall A. LaViolette, and Cynthia A. Phillips. 2011. Tolerating the community detection resolution limit with edge weighting. Phys. Rev. E 83 (May 2011), 056119. Issue 5.Google ScholarGoogle ScholarCross RefCross Ref
  8. J. Bondy and M. Simonovits. 1974. Cycles of even length in graphs. Journal of Combinatorial Theory, Series B (1974), 97--105.Google ScholarGoogle Scholar
  9. Vladimir Braverman, Rafail Ostrovsky, and Dan Vilenchik. 2013. How Hard Is Counting Triangles in the Streaming Model?. In Automata, Languages, and Programming - 40th International Colloquium, ICALP 2013, Riga, Latvia, July 8--12, 2013, Proceedings, Part I. 244--254. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Joshua Brody and Amit Chakrabarti. 2008. Sublinear communication protocols for multi-party pointer jumping and a related lower bound. arXiv preprint arXiv:0802.2843 (2008).Google ScholarGoogle Scholar
  11. Joshua Brody and Mario Sanchez. 2015. Dependent Random Graphs and Multiparty Pointer Jumping. CoRR abs/1506.01083 (2015). arXiv:1506.01083Google ScholarGoogle Scholar
  12. Luciana S. Buriol, Gereon Frahling, Stefano Leonardi, Alberto Marchetti-Spaccamela, and Christian Sohler. 2006. Counting triangles in data streams. In Proceedings of the Twenty-Fifth ACM SIGACTSIGMOD- SIGART Symposium on Principles of Database Systems, June 26--28, 2006, Chicago, Illinois, USA. 253--262. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Graham Cormode and Hossein Jowhari. 2014. A second look at counting triangles in graph streams. Theor. Comput. Sci. 552 (2014), 44--51.Google ScholarGoogle ScholarCross RefCross Ref
  14. Jean-Pierre Eckmann and Elisha Moses. 2002. Curvature of co-links uncovers hidden thematic layers in the World Wide Web. Proceedings of the National Academy of Sciences 99, 9 (2002), 5825--5829.Google ScholarGoogle ScholarCross RefCross Ref
  15. emab (http://math.stackexchange.com/users/74964/emab). 2014. Number of triangles in a graph based on number of edges. Mathematics Stack Exchange. URL:http://math.stackexchange.com/q/823650 (version: 2014-06-07).Google ScholarGoogle Scholar
  16. David GarcÃa-Soriano and Konstantin Kutzkov. {n. d.}. Triangle counting in streamed graphs via small vertex covers. In Proceedings of the 2014 SIAM International Conference on Data Mining. 352--360.Google ScholarGoogle Scholar
  17. Hossein Jowhari and Mohammad Ghodsi. 2005. New Streaming Algorithms for Counting Triangles in Graphs. In Computing and Combinatorics, 11th Annual International Conference, COCOON 2005, Kunming, China, August 16--29, 2005, Proceedings. 710--716.Google ScholarGoogle Scholar
  18. John Kallaugher and Eric Price. 2017. A Hybrid Sampling Scheme for Triangle Counting. In Proceedings of the Twenty-Eighth Annual ACM-SIAM Symposium on Discrete Algorithms (SODA '17). Society for Industrial and Applied Mathematics, Philadelphia, PA, USA, 1778--1797. http://dl.acm.org/citation.cfm?id=3039686.3039802Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. B. Kalyanasundaram and G. Schintger. 1992. The Probabilistic Communication Complexity of Set Intersection. SIAM Journal on Discrete Mathematics 5, 4 (1992), 545--557.Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Daniel M. Kane, Kurt Mehlhorn, Thomas Sauerwald, and He Sun. 2012. Counting Arbitrary Subgraphs in Data Streams. In Proceedings of the 39th International Colloquium Conference on Automata, Languages, and Programming - Volume Part II (ICALP'12). Springer-Verlag, Berlin, Heidelberg, 598--609. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Mihail N. Kolountzakis, Gary L. Miller, Richard Peng, and Charalampos E. Tsourakakis. 2012. Efficient Triangle Counting in Large Graphs via Degree-Based Vertex Partitioning. Internet Mathematics 8, 1--2 (2012), 161--185.Google ScholarGoogle ScholarCross RefCross Ref
  22. Ilan Kremer, Noam Nisan, and Dana Ron. 1995. On Randomized One-round Communication Complexity. In Proceedings of the Twentyseventh Annual ACM Symposium on Theory of Computing (STOC '95). ACM, New York, NY, USA, 596--605. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Konstantin Kutzkov and Rasmus Pagh. 2014. Triangle Counting in Dynamic Graph Streams. In Algorithm Theory - SWAT 2014 - 14th Scandinavian Symposium and Workshops, Copenhagen, Denmark, July 2--4, 2014. Proceedings. 306--318.Google ScholarGoogle Scholar
  24. Jure Leskovec, Lars Backstrom, Ravi Kumar, and Andrew Tomkins. 2008. Microscopic evolution of social networks. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Las Vegas, Nevada, USA, August 24--27, 2008. 462--470. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Madhusudan Manjunath, Kurt Mehlhorn, Konstantinos Panagiotou, and He Sun. 2011. Approximate Counting of Cycles in Streams. In Algorithms - ESA 2011 - 19th Annual European Symposium, Saarbrücken, Germany, September 5--9, 2011. Proceedings. 677--688. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Andrew McGregor, Sofya Vorotnikova, and Hoa T. Vu. 2016. Better Algorithms for Counting Triangles in Data Streams. In Proceedings of the 35th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems, PODS 2016, San Francisco, CA, USA, June 26 - July 01, 2016. 401--411. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Rasmus Pagh and Charalampos E. Tsourakakis. 2012. Colorful triangle counting and a MapReduce implementation. Inf. Process. Lett. 112, 7 (2012), 277--281. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. A. Pavan, Kanat Tangwongsan, Srikanta Tirthapura, and Kun-Lung Wu. 2013. Counting and Sampling Triangles from a Graph Stream. PVLDB 6, 14 (2013), 1870--1881. http://www.vldb.org/pvldb/vol6/ p1870-aduri.pdf Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. A. A. Razborov. 1992. On the Distributional Complexity of Disjointness. Theor. Comput. Sci. 106, 2 (Dec. 1992), 385--390. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Alexander A. Sherstov. 2014. Communication Lower Bounds Using Directional Derivatives. J. ACM 61, 6, Article 34 (Dec. 2014), 71 pages. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Alexander A Sherstov. 2016. The multiparty communication complexity of set disjointness. SIAM J. Comput. 45, 4 (2016), 1450--1489.Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Siddharth Suri and Sergei Vassilvitskii. 2011. Counting triangles and the curse of the last reducer. In Proceedings of the 20th International Conference on World Wide Web, WWW 2011, Hyderabad, India, March 28 - April 1, 2011. 607--614. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Charalampos E. Tsourakakis, Mihail N. Kolountzakis, and Gary L. Miller. 2011. Triangle Sparsifiers. J. Graph Algorithms Appl. 15, 6 (2011), 703--726.Google ScholarGoogle ScholarCross RefCross Ref
  34. Emanuele Viola and Avi Wigderson. 2009. One-way multiparty communication lower bound for pointer jumping with applications. Combinatorica 29, 6 (01 Nov 2009), 719--743. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. The Complexity of Counting Cycles in the Adjacency List Streaming Model

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Conferences
      PODS '19: Proceedings of the 38th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems
      June 2019
      494 pages
      ISBN:9781450362276
      DOI:10.1145/3294052

      Copyright © 2019 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 25 June 2019

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article

      Acceptance Rates

      PODS '19 Paper Acceptance Rate29of87submissions,33%Overall Acceptance Rate642of2,707submissions,24%

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader