Triangle Counting in Dynamic Graph Streams

Estimating the number of triangles in graph streams using a limited amount of memory has become a popular topic in the last decade. Different variations of the problem have been studied, depending on whether the graph edges are provided in an arbitrary order or as incidence lists. However, with a fe...

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Vydané v:Algorithmica Ročník 76; číslo 1; s. 259 - 278
Hlavní autori: Bulteau, Laurent, Froese, Vincent, Kutzkov, Konstantin, Pagh, Rasmus
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Springer US 01.09.2016
Springer Verlag
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Abstract Estimating the number of triangles in graph streams using a limited amount of memory has become a popular topic in the last decade. Different variations of the problem have been studied, depending on whether the graph edges are provided in an arbitrary order or as incidence lists. However, with a few exceptions, the algorithms have considered insert-only streams. We present a new algorithm estimating the number of triangles in dynamic graph streams where edges can be both inserted and deleted. We show that our algorithm achieves better time and space complexity than previous solutions for various graph classes, for example sparse graphs with a relatively small number of triangles. Also, for graphs with constant transitivity coefficient, a common situation in real graphs, this is the first algorithm achieving constant processing time per edge. The result is achieved by a novel approach combining sampling of vertex triples and sparsification of the input graph. In the course of the analysis of the algorithm we present a lower bound on the number of pairwise independent 2-paths in general graphs which might be of independent interest. At the end of the paper we discuss lower bounds on the space complexity of triangle counting algorithms that make no assumptions on the structure of the graph.
AbstractList Estimating the number of triangles in graph streams using a limited amount of memory has become a popular topic in the last decade. Different variations of the problem have been studied, depending on whether the graph edges are provided in an arbitrary order or as incidence lists. However, with a few exceptions, the algorithms have considered insert-only streams. We present a new algorithm estimating the number of triangles in dynamic graph streams where edges can be both inserted and deleted. We show that our algorithm achieves better time and space complexity than previous solutions for various graph classes, for example sparse graphs with a relatively small number of triangles. Also, for graphs with constant transitivity coefficient, a common situation in real graphs, this is the first algorithm achieving constant processing time per edge. The result is achieved by a novel approach combining sampling of vertex triples and sparsification of the input graph. In the course of the analysis of the algorithm we present a lower bound on the number of pairwise independent 2-paths in general graphs which might be of independent interest. At the end of the paper we discuss lower bounds on the space complexity of triangle counting algorithms that make no assumptions on the structure of the graph.
Estimating the number of triangles in graph streams using a limited amount of memory has become a popular topic in the last decade. Different variations of the problem have been studied, depending on whether the graph edges are provided in an arbitrary order or as incidence lists. However, with a few exceptions, the algorithms have considered insert-only streams. We present a new algorithm estimating the number of triangles in dynamic graph streams where edges can be both inserted and deleted. We show that our algorithm achieves better time and space complexity than previous solutions for various graph classes, for example sparse graphs with a relatively small number of triangles. Also, for graphs with constant transitivity coefficient, a common situation in real graphs, this is the first algorithm achieving constant processing time per edge. The result is achieved by a novel approach combining sampling of vertex triples and sparsification of the input graph. In the course of the analysis of the algorithm we present a lower bound on the number of pairwise independent 2-paths in general graphs which might be of independent interest. At the end of the paper we discuss lower bounds on the space complexity of triangle counting algorithms that make no assumptions on the structure of the graph.
Author Bulteau, Laurent
Froese, Vincent
Kutzkov, Konstantin
Pagh, Rasmus
Author_xml – sequence: 1
  givenname: Laurent
  surname: Bulteau
  fullname: Bulteau, Laurent
  organization: Technische Universität Berlin, Inria, LBBE
– sequence: 2
  givenname: Vincent
  surname: Froese
  fullname: Froese, Vincent
  organization: Technische Universität Berlin
– sequence: 3
  givenname: Konstantin
  surname: Kutzkov
  fullname: Kutzkov, Konstantin
  email: kutzkov@gmail.com
  organization: NEC Laboratories Europe
– sequence: 4
  givenname: Rasmus
  surname: Pagh
  fullname: Pagh, Rasmus
  organization: IT University of Copenhagen
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Issue 1
Keywords Streaming algorithms
Triangle counting
Randomized approximation algorithms
Language English
License Distributed under a Creative Commons Attribution 4.0 International License: http://creativecommons.org/licenses/by/4.0
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Jha, M., Seshadhri, C., Pinar, A.: A space efficient streaming algorithm for triangle counting using the birthday paradox. In: The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013, Chicago, IL, USA, August 11–14, 589–597 (2013)
Aiello, W., Chung, F.R.K., Lu, L.: A Random Gmodel for Massive Graphs. In: Proceedings of the Thirty-Second Annual ACM Symposium on Theory of Computing, May 21–23, 2000, Portland, OR, USA (STOC), 171–180 (2000)
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PaghRRasmusTCharalmposEColorful triangle counting and a MapReduce implementationInf. Process. Lett.20121127277281287916510.1016/j.ipl.2011.12.0071237.68245
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PavanATangwongsanKTirthapuraSWuKLCounting and sampling triangles from a graph streamPVLDB201361418701881
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TsourakakisCEKolountzakisMNGaryGLTriangle sparsifiersJ. Graph Algorithms Appl.2011156703726284497810.7155/jgaa.002451276.05120
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References_xml – reference: Jha, M., Seshadhri, C., Pinar, A.: A space efficient streaming algorithm for triangle counting using the birthday paradox. In: The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013, Chicago, IL, USA, August 11–14, 589–597 (2013)
– reference: AlonNYusterRZwickUFinding and counting given length cyclesAlgorithmica1997173209223142573410.1007/BF025231890865.68093
– reference: Ahn, K.J., Guha, S., McGregor, A.: Graph Sketches: Sparsification, Spanners, and Subgraphs. In: Proceedings of the 31st ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, PODS 5–14 (2012)
– reference: Jowhari, H., Ghodsi, M.: New Streaming Algorithms for Counting Triangles in Graphs. In: Computing and Combinatorics, 11th Annual International Conference, COCOON 2005, Kunming, China, August 16–29, 710–716 (2005)
– reference: CarterLJWegmanMNUniversal classes of hash functionsJ. Comput. Syst. Sci.197918214315453217310.1016/0022-0000(79)90044-80412.68090
– reference: KolountzakisMNMillerGLPengRRichardTCharalamposEEfficient triangle counting in large graphs via degree-based vertex partitioningInt. Math.201281–216118529004921245.05120
– reference: BerryJWHendricksonBLaVioletteRAPhillipsCATolerating the community detection resolution limit with edge weightingPhys. Rev. E20118355611910.1103/PhysRevE.83.056119
– reference: Manjunath, M., Mehlhorn, K., Panagiotou, K., Sun, H.: Approximate Counting of Cycles in Streams. In: Algorithms—ESA 2011—19th Annual European Symposium, Saarbrücken, Germany, September 5–9, 677–688 (2011)
– reference: PavanATangwongsanKTirthapuraSWuKLCounting and sampling triangles from a graph streamPVLDB201361418701881
– reference: Williams, V.V.: Multiplying Matrices Faster than Coppersmith-Winograd. In: Proceedings of the 44th Symposium on Theory of Computing Conference, STOC 2012, New York, NY, USA, May 19–22, 887–898 (2012)
– reference: AlbertRBarabasiALStatistical mechanics of complex networksRev. Mod. Phys.2002744797189509610.1103/RevModPhys.74.471205.82086
– reference: Frahling, G., Indyk, P., Sohler, C.: Sampling in dynamic data streams and applications. In: Symposium on Computational Geometry 142–149 (2005)
– reference: Buriol, L.S., Frahling, G., Leonardi, S., Marchetti-Spaccamela, A., Sohler, C.: Counting triangles in data streams. In: Proceedings of the Twenty-Fifth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, June 26–28, Chicago, Illinois, USA, 253–262 (2006)
– reference: PaghRRasmusTCharalmposEColorful triangle counting and a MapReduce implementationInf. Process. Lett.20121127277281287916510.1016/j.ipl.2011.12.0071237.68245
– reference: PǎtraşcuMThorupMThe power of simple tabulation hashingJ. ACM20125931429462181281.68089
– reference: Kane, D.M., Mehlhorn, K., Sauerwald, T., Sun, H.: Counting Arbitrary Subgraphs in Data Streams. In: Automata, Languages, and Programming—39th International Colloquium, ICALP: Warwick, UK, July 9–13, Proceedings. Part II 598–609 (2012)
– reference: Tsourakakis, C.E., Kang, U., Miller, G.L., Faloutsos, C.: DOULION: Counting Triangles in Massive Graphs with a Coin. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France, June 28–July 1, 837–846 (2009)
– reference: TsourakakisCEKolountzakisMNGaryGLTriangle sparsifiersJ. Graph Algorithms Appl.2011156703726284497810.7155/jgaa.002451276.05120
– reference: Leonardi, S.: List of Open Problems in Sublinear Algorithms: Problem 11. http://sublinear.info/11
– reference: Jowhari, H., Saglam, M., Tardos, G.: Tight bounds for Lp samplers, finding duplicates in streams, and related problems. In: Proceedings of the 30th ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, PODS 2011, June 12–16, Athens, Greece, 49–58 (2011)
– reference: Seshadhri, C., Pinar, A., Kolda, T.: Triadic Measures on Graphs: The Power of Wedge Sampling. In: Proceedings of the 13th SIAM International Conference on Data Mining, May 2–4. Austin, Texas, USA, 10–18 (2013)
– reference: Arbitman, Y., Naor, M., Segev, G.: Backyard Cuckoo Hashing: Constant Worst-Case Operations with a Succinct Representation. In: 51th Annual IEEE Symposium on Foundations of Computer Science, FOCS 2010, October 23–26, 2010, Las Vegas, Nevada, USA, 787–796 (2010)
– reference: BecchettiLBoldiPCastilloCGionisAEfficient algorithms for large-scale local triangle countingACM Trans. Knowl. Discov. Data20101311328
– reference: Aiello, W., Chung, F.R.K., Lu, L.: A Random Gmodel for Massive Graphs. In: Proceedings of the Thirty-Second Annual ACM Symposium on Theory of Computing, May 21–23, 2000, Portland, OR, USA (STOC), 171–180 (2000)
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Snippet Estimating the number of triangles in graph streams using a limited amount of memory has become a popular topic in the last decade. Different variations of the...
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SubjectTerms Algorithm Analysis and Problem Complexity
Algorithms
Bioinformatics
Computational Complexity
Computer Science
Computer Systems Organization and Communication Networks
Data Structures and Algorithms
Data Structures and Information Theory
Mathematics of Computing
Theory of Computation
Title Triangle Counting in Dynamic Graph Streams
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Volume 76
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