Encyclopedia of Big Data Technologies

2019 Edition
| Editors: Sherif Sakr, Albert Y. Zomaya

Graph Benchmarking

  • Khaled AmmarEmail author
Reference work entry
DOI: https://doi.org/10.1007/978-3-319-77525-8_298


Graphs have attracted research efforts for about three centuries (Biggs et al. 1986). Earlier, the focus was mainly on mathematical models and algorithms. Graph theory continues to be an active research area that includes many open problems. In the last decade, there has been a considerable amount of research in analyzing and processing large graph structures for real applications. This effort has led to the development of several alternative algorithms, techniques, and big data systems.

Parallel systems are very important and big data challenges are relevant to graph analytics. Although the edge list of some existing graphs are relatively small in size and may fit in one machine, these graphs are often large when considering vertex properties, graph indexes, and intermediate results during query processing. Processing often leads to very large memory requirements, which makes it not practical to fit the entire graph and its data into a single machine.

Big graph data has a...

This is a preview of subscription content, log in to check access.


  1. Akoglu L, Faloutsos C (2009) RTG: a recursive realistic graph generator using random typing. Data Min Know Disc 19(2):194–209MathSciNetCrossRefGoogle Scholar
  2. Albert R, Barabási AL (2002) Statistical mechanics of complex networks. Rev Mod Phys 74(1):47MathSciNetzbMATHCrossRefGoogle Scholar
  3. Ammar K, Özsu M (2014) WGB: towards a universal graph benchmark. In: Rabl T, Raghunath N, Poess M, Bhandarkar M, Jacobsen HA, Baru C (eds) Advancing big data benchmarks. Lecture notes in computer science. Springer, pp 58–72. https://doi.org/10.1007/978-3-319-10596-3_6Google Scholar
  4. Anderson MJ, Sundaram N, Satish N, Patwary MMA, Willke TL, Dubey P (2016) Graphpad: optimized graph primitives for parallel and distributed platforms. In: Proceedings of the 30th international parallel and distributed processing symposium, pp 313–322Google Scholar
  5. Bader DA, Madduri K (2005) Design and implementation of the hpcs graph analysis benchmark on symmetric multiprocessors. In: International conference on high-performance computing, pp 465–476Google Scholar
  6. Batarfi O, Shawi R, Fayoumi A, Nouri R, Beheshti SMR, Barnawi A, Sakr S (2015) Large scale graph processing systems: survey and an experimental evaluation. Clust Comput 18(3):1189–1213CrossRefGoogle Scholar
  7. Beamer S, Asanović K, Patterson D (2015) The gap benchmark suite. arXiv preprint arXiv:150803619Google Scholar
  8. Biggs N, Lloyd EK, Wilson RJ (1986) Graph theory. Clarendon Press, New York, pp 1736–1936Google Scholar
  9. Boldi P, Vigna S (2004) The webgraph framework I: compression techniques, pp 595–601Google Scholar
  10. Boldi P, Codenotti B, Santini M, Vigna S (2004) Ubicrawler: a scalable fully distributed web crawler. Softw Pract Exp 34(8):711–726CrossRefGoogle Scholar
  11. Boldi P, Rosa M, Santini M, Vigna S (2011) Layered label propagation: a multiresolution coordinate-free ordering for compressing social networks, In: Proceedings of the 20th international conference on world wide web, pp 587–596Google Scholar
  12. Chakrabarti D, Faloutsos C, McGlohon M (2010) Graph mining: laws and generators. In: Aggarwal CC (ed) Managing and mining graph data. Springer, pp 69–123Google Scholar
  13. Ciglan M, Averbuch A, Hluchy L (2012) Benchmarking traversal operations over graph databases. In: Proceedings of the workshops of 28th international conference on data engineering. IEEE, pp 186–189Google Scholar
  14. Erdös P, Rényi A (1960) On the evolution of random graphs. In: Publication of the mathematical institute of the hungarian academy of sciences, pp 17–61zbMATHGoogle Scholar
  15. Erling O, Averbuch A, Larriba-Pey J, Chafi H, Gubichev A, Prat A, Pham MD, Boncz P (2015) The LDBC social network benchmark: interactive workload, In: Proceedings of the 2015 ACM SIGMOD international conference on management of data. ACM, pp 619–630Google Scholar
  16. Faloutsos M, Faloutsos P, Faloutsos C (1999) On power-law relationships of the internet topology. In: ACM SIGCOMM computer communication review, vol 29. ACM, pp 251–262Google Scholar
  17. Han M, Daudjee K, Ammar K, Özsu MT, Wang X, Jin T (2014) An experimental comparison of pregel-like graph processing systems. Proc VLDB Endow 7(12):1047–1058CrossRefGoogle Scholar
  18. Hong S, Depner S, Manhardt T, Lugt JVD, Verstraaten M, Chafi H (2015) Pgx.d: a fast distributed graph processing engine. In: Proceedings of international conference for high performance computing, networking, storage and analysis, pp 1–12Google Scholar
  19. Iosup A, Hegeman T, Ngai WL, Heldens S, Prat-Pérez A, Manhardto T, Chafio H, Capotă M, Sundaram N, Anderson M et al (2016) LDBC graphalytics: a benchmark for large-scale graph analysis on parallel and distributed platforms. Proc VLDB Endow 9(13): 1317–1328CrossRefGoogle Scholar
  20. Leskovec J, Krevl A (2014) SNAP Datasets: stanford large network dataset collection. http://snap.stanford.edu/data
  21. Leskovec J, Chakrabarti D, Kleinberg J, Faloutsos C (2005a) Realistic, mathematically tractable graph generation and evolution, using kronecker multiplication. In: Proceedings of the 9th European conference on principles of data mining and knowledge discovery, vol 5, pp 133–145CrossRefGoogle Scholar
  22. Leskovec J, Kleinberg J, Faloutsos C (2005b) Graphs over time: Densification laws, shrinking diameters and possible explanations. In: Proceedings of the 11th ACM SIGKDD international conference on knowledge discovery and data mining, pp 177–187Google Scholar
  23. Low Y, Gonzalez J, Kyrola A, Bickson D, Guestrin C, Hellerstein JM (2012) Distributed GraphLab: a framework for machine learning in the cloud. Proc VLDB Endow 5(8):716–727CrossRefGoogle Scholar
  24. Lu Y, Cheng J, Yan D, Wu H (2014) Large-scale distributed graph computing systems: an experimental evaluation. Proc VLDB Endow 8(3):281–292CrossRefGoogle Scholar
  25. Malewicz G, Austern MH, Bik AJ, Dehnert JC, Horn I, Leiser N, Czajkowski G (2010) Pregel: a system for large-scale graph processing. In: Proceedings of ACM SIGMOD international conference on management of data, pp 135–146Google Scholar
  26. McSherry F, Isard M, Murray DG (2015) Scalability! but at what cost? In: Proceedings of the 15th USENIX conference on hot topics in operating systemsGoogle Scholar
  27. Miller GA (1957) Some effects of intermittent silence. Am J Psychol 70(2):311–314CrossRefGoogle Scholar
  28. Murphy RC, Wheeler KB, Barrett BW, Ang JA (2010) Introducing the graph 500. Cray Users Group (CUG)Google Scholar
  29. Wang L, Zhan J, Luo C, Zhu Y, Yang Q, He Y, Gao W, Jia Z, Shi Y, Zhang S, Zheng C, Lu G, Zhan K, Li X, Qiu B (2014) Bigdatabench: a big data benchmark suite from internet services. In: International symposium on high performance computer architecture, pp 488–499Google Scholar
  30. Watts DJ, Strogatz SH (1998) Collective dynamics of small-worldnetworks. Nature 393(6684): 440–442zbMATHCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Thomson Reuters LabsThomson ReutersWaterlooCanada