Skip to main content
SpringerLink
Log in

Graph Benchmarking

  • Living reference work entry
  • First Online:
Encyclopedia of Big Data Technologies

  • 129 Accesses

Introduction

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 via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

References

  • Akoglu L, Faloutsos C (2009) RTG: a recursive realistic graph generator using random typing. Data Min Know Disc 19(2):194–209

    Article  MathSciNet  Google Scholar 

  • Albert R, Barabási AL (2002) Statistical mechanics of complex networks. Rev Mod Phys 74(1):47

    Article  MathSciNet  Google Scholar 

  • 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_6

    Google Scholar 

  • 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 313–322

    Google Scholar 

  • 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 465–476

    Google Scholar 

  • 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):1189–1213

    Google Scholar 

  • Beamer S, Asanović K, Patterson D (2015) The gap benchmark suite. arXiv preprint arXiv:150803619

    Google Scholar 

  • Biggs N, Lloyd EK, Wilson RJ (1986) Graph theory. Clarendon Press, New York, pp 1736–1936

    Google Scholar 

  • Boldi P, Vigna S (2004) The webgraph framework I: compression techniques, pp 595–601

    Google Scholar 

  • Boldi P, Codenotti B, Santini M, Vigna S (2004) Ubicrawler: a scalable fully distributed web crawler. Softw Pract Exp 34(8):711–726

    Google Scholar 

  • 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 587–596

    Google Scholar 

  • Chakrabarti D, Faloutsos C, McGlohon M (2010) Graph mining: laws and generators. In: Aggarwal CC (ed) Managing and mining graph data. Springer, pp 69–123

    Chapter  Google Scholar 

  • 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 186–189

    Google Scholar 

  • Erdös P, Rényi A (1960) On the evolution of random graphs. In: Publication of the mathematical institute of the hungarian academy of sciences, pp 17–61

    Google Scholar 

  • 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 619–630

    Google Scholar 

  • 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 251–262

    Article  Google Scholar 

  • 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):1047–1058

    Article  Google Scholar 

  • 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 1–12

    Google Scholar 

  • 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): 1317–1328

    Article  Google Scholar 

  • Leskovec J, Krevl A (2014) SNAP Datasets: stanford large network dataset collection. http://snap.stanford.edu/data

  • 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 133–145

    Chapter  Google Scholar 

  • 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 177–187

    Google Scholar 

  • 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):716–727

    Article  Google Scholar 

  • Lu Y, Cheng J, Yan D, Wu H (2014) Large-scale distributed graph computing systems: an experimental evaluation. Proc VLDB Endow 8(3):281–292

    Article  Google Scholar 

  • 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 135–146

    Google Scholar 

  • McSherry F, Isard M, Murray DG (2015) Scalability! but at what cost? In: Proceedings of the 15th USENIX conference on hot topics in operating systems

    Google Scholar 

  • Miller GA (1957) Some effects of intermittent silence. Am J Psychol 70(2):311–314

    Article  Google Scholar 

  • Murphy RC, Wheeler KB, Barrett BW, Ang JA (2010) Introducing the graph 500. Cray Users Group (CUG)

    Google Scholar 

  • 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 488–499

    Google Scholar 

  • Watts DJ, Strogatz SH (1998) Collective dynamics of small-worldnetworks. Nature 393(6684):440–442

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Thomson Reuters Labs, Thomson Reuters, Waterloo, ON, Canada

    Khaled Ammar

Authors
  1. Khaled Ammar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Khaled Ammar .

Editor information

Editors and Affiliations

  1. Institute of Computer Science, University of Tartu, Tartu, Estonia

    Sherif Sakr

  2. Sch of Info Techno, Building J12, University of Sydney Sch of Info Techno, Building J12, Sydney, Australia

    Albert Zomaya

Section Editor information

  1. Server Technologies, Oracle, Redwood Shores, California, USA

    Meikel Poess

  2. Database Systems and Information Management Group, Technische Universität Berlin, Berlin, Germany

    Tilmann Rabl

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Ammar, K. (2018). Graph Benchmarking. In: Sakr, S., Zomaya, A. (eds) Encyclopedia of Big Data Technologies. Springer, Cham. https://doi.org/10.1007/978-3-319-63962-8_298-1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-63962-8_298-1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63962-8

  • Online ISBN: 978-3-319-63962-8

  • eBook Packages: Springer Reference MathematicsReference Module Computer Science and Engineering

Publish with us

Policies and ethics

Search

Navigation