Encyclopedia of Social Network Analysis and Mining

2014 Edition
| Editors: Reda Alhajj, Jon Rokne

Benchmarking for Graph Clustering and Partitioning

  • David A. Bader
  • Henning Meyerhenke
  • Peter Sanders
  • Christian Schulz
  • Andrea Kappes
  • Dorothea Wagner
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-6170-8_23




Performance evaluation for comparison to the state of the art

Benchmark Suite

Set of instances used for benchmarking


Benchmarking refers to a repeatable performance evaluation as a means to compare somebody’s work to the state of the art in the respective field. As an example, benchmarking can compare the computing performance of new and old hardware.

In the context of computing, many different benchmarks of various sorts have been used. A prominent example is the Linpack benchmark of the TOP500 list of the fastest computers in the world, which measures the performance of the hardware by solving a dense linear algebra problem. Different categories of benchmarks include sequential vs. parallel, microbenchmark vs. application, or fixed code vs. informal problem description. See, e.g., Weicker (2002) for a more detailed treatment of hardware evaluation.

When it comes to benchmarking...

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  1. Aloise D, Caporossi G, Perron S, Hansen P, Liberti L, Ruiz M (2012) Modularity maximization in networks by variable neighborhood search. In: 10th DIMACS implementation challenge workshop. Georgia Institute of Technology, AtlantaGoogle Scholar
  2. Arenas A (2009) Network data sets. http://deim.urv.cat/aarenas/data/welcome.htm. Online; accessed 28 Sept 2012
  3. Bader DA, Berry J, Kahan S, Murphy R, Jason Riedy E, Willcock J (2010) Graph 500 benchmark 1 (“search”), version 1.1. Technical report, Graph 500Google Scholar
  4. Bader D, Meyerhenke H, Sanders P, Wagner D (2012) 10th DIMACS implementation challenge. http://www.cc.gatech.edu/dimacs10/. Online; accessed 28 Sept 2012
  5. Barabási A-L, Albert R (1999) Emergence of scaling in random networks. Science 286:509–512CrossRefMathSciNetGoogle Scholar
  6. Bauer R, Delling D, Sanders P, Schieferdecker D, Schultes D, Wagner D (2010) Combining hierarchical and goal-directed speed-up techniques for Dijkstra’s algorithm. ACM J Exp Algorithmics 15: 2.3:2.1–2.3:2.31Google Scholar
  7. Berry JW, Hendrickson B, LaViolette RA, Phillips CA (2011) Tolerating the community detection resolution limit with edge weighting. Phys Rev E 83:056119CrossRefGoogle Scholar
  8. Bollobás B (1985) Random graphs. Academic, LondonzbMATHGoogle Scholar
  9. Çatalyürek ÜV, Aykanat C (1996) Decomposing irregularly sparse matrices for parallel matrix-vector multiplication. In: Ferreira A, Rolim J, Saad Y, Yang T (eds) Parallel algorithms for irregularly structured problems. Volume 1117 of lecture notes in computer science. Springer, Berlin/Heidelberg, pp 75–86. doi:10.1007/BFb0030098Google Scholar
  10. Çatalyürek ÜV, Kaya K, Langguth J, Uçar B (2012) A divisive clustering technique for maximizing the modularity. In: 10th DIMACS implementation challenge workshop. Georgia Institute of Technology, AtlantaGoogle Scholar
  11. Chakrabarti D, Zhan Y, Faloutsos C (2004) R-MAT: a recursive model for graph mining. In: Proceedings of the 4th SIAM international conference on data mining (SDM), Orlando, April 2004. SIAMGoogle Scholar
  12. Davis T (2008) The University of Florida Sparse Matrix Collection. http://www.cise.ufl.edu/research/sparse/matrices. Online; accessed 28 Sept 2012
  13. Fagginger Auer BO, Bisseling RH (2012) Graph coarsening and clustering on the GPU. In: 10th DIMACS implementation challenge workshop. Georgia Institute of Technology, AtlantaGoogle Scholar
  14. Fortunato S, Barthelemy M (2007) Resolution limit in community detection. Proc Natl Acad Sci 104:36–41CrossRefGoogle Scholar
  15. Gilbert H (1959) Random graphs. Ann Math Stat 30(4):1141–1144CrossRefzbMATHGoogle Scholar
  16. Good BH, de Montjoye Y-A, Clauset A (2010) Performance of modularity maximization in practical contexts. Phys Rev E 81:046106CrossRefMathSciNetGoogle Scholar
  17. Kannan R, Vempala S, Vetta A (2004) On clusterings: good, bad, spectral. J ACM 51(3):497–515CrossRefzbMATHMathSciNetGoogle Scholar
  18. Karypis G, Kumar V (1999) A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM J Sci Comput 20:359–392CrossRefzbMATHMathSciNetGoogle Scholar
  19. Lancichinetti A, Fortunato S (2009) Community detection algorithms: a comparative analysis. Phys Rev E 80(5):056117CrossRefGoogle Scholar
  20. Lancichinetti A, Fortunato S (2011) Limits of modularity maximization in community detection. Phys Rev E 84:066122CrossRefGoogle Scholar
  21. Lescovec J. Stanford Network Analysis Package (SNAP). http://snap.stanford.edu/index.html. Online; accessed 28 Sept 2012
  22. Newman M () Network data. http://www-personal.umich.edu/mejn/netdata/. Online; accessed 28 Sept 2012
  23. Ovelgönne M, Geyer-Schulz A (2012) An ensemble learning strategy for graph clustering. In: 10th DIMACS implementation challenge workshop. Georgia Institute of Technology, AtlantaGoogle Scholar
  24. Riedy EJ, Meyerhenke H, Ediger D, Bader DA (2012) Parallel community detection for massive graphs. In: 10th DIMACS implementation challenge workshop. Georgia Institute of Technology, AtlantaGoogle Scholar
  25. Seshadhri C, Kolda TG, Pinar A (2012) Community structure and scale-free collections of Erdős-Rényi graphs. Phys Rev E 85(5):066122CrossRefGoogle Scholar
  26. Soper AJ, Walshaw C, Cross M (2004) A combined evolutionary search and multilevel optimisation approach to graph-partitioning. J Glob Optim 29(2): 225–241CrossRefzbMATHMathSciNetGoogle Scholar
  27. van Dongen SM (2000) Graph clustering by flow simulation. PhD thesis, University of UtrechtGoogle Scholar
  28. Watts DJ, Strogatz SH (1998) Collective dynamics of “Small-World” networks. Nature 393: 440–442CrossRefGoogle Scholar
  29. Weicker R (2002) Benchmarking. In: Calzarossa M, Tucci S (eds) Performance evaluation of complex systems: techniques and tools. Volume 2459 of lecture notes in computer science. Springer, Berlin/Heidelberg, pp 231–242Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • David A. Bader
    • 1
  • Henning Meyerhenke
    • 2
  • Peter Sanders
    • 2
  • Christian Schulz
    • 2
  • Andrea Kappes
    • 2
  • Dorothea Wagner
    • 2
  1. 1.School of Computational Science and Engineering, Georgia Institute of TechnologyAtlantaUSA
  2. 2.Karlsruhe Institute of Technology (KIT), Institute of Theoretical InformaticsKarlsruheGermany