Skip to main content
SpringerLink
Log in

Efficient Batched Distance, Closeness and Betweenness Centrality Computation in Unweighted and Weighted Graphs

  • Fachbeitrag
  • Published:
Datenbank-Spektrum Aims and scope Submit manuscript

Abstract

Distance and centrality computations are important building blocks for modern graph databases as well as for dedicated graph analytics systems. Two commonly used centrality metrics are the compute-intense closeness and betweenness centralities, which require numerous expensive shortest distance calculations. We propose batched algorithm execution to run multiple distance and centrality computations at the same time and let them share common graph and data accesses. Batched execution amortizes the high cost of random memory accesses and presents new vectorization potential on modern CPUs and compute accelerators. We show how batched algorithm execution can be leveraged to significantly improve the performance of distance, closeness, and betweenness centrality calculations on unweighted and weighted graphs. Our evaluation demonstrates that batched execution can improve the runtime of these common metrics by over an order of magnitude.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Notes

  1. This paper is an extended version of the previously published [18].

  2. https://github.com/ldbc/ldbc_snb_datagen

  3. https://github.com/graph500/graph500

References

  1. Akiba T, Iwata Y, Yoshida Y (2013) Fast exact shortest-path distance queries on large networks by pruned landmark labeling. In: Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data. ACM, New York, pp 349–360

    Chapter  Google Scholar 

  2. Bader DA, Kintali S, Madduri K, Mihail M (2007) Approximating betweenness centrality. In: International Workshop on Algorithms and Models for the Web-Graph. Springer, Berlin Heidelberg, pp 124–137

    Chapter  Google Scholar 

  3. Bellman R (1958) On a routing problem. Q Appl Math 87–90. doi:10.1090/qam/102435

    MATH  Google Scholar 

  4. Brandes U (2001) A faster algorithm for betweenness centrality. J Math Sociol 25:163–177

    Article  MATH  Google Scholar 

  5. Brin S, Page L (1998) The Anatomy of a Large-Scale Hypertextual Web Search Engine. In: Seventh International World-Wide Web Conference (WWW 1998), pp 3825–3833

    Google Scholar 

  6. Eppstein D, Wang J (2001) Fast approximation of centrality. In: Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms. Society for Industrial and Applied Mathematics, Philadelphia, pp 228–229

  7. Fredman ML, Tarjan RE (1987) Fibonacci heaps and their uses in improved network optimization algorithms. J ACM 34(3):596–615. doi:10.1145/28869.28874

    Article  MathSciNet  Google Scholar 

  8. Freeman LC (1978) Centrality in social networks conceptual clarification. Soc Networks 1(3):215–239

    Article  Google Scholar 

  9. Hong S, Depner S, Manhardt T, Van Der Lugt J, Verstraaten M, Chafi H (2015) Pgx. d: a fast distributed graph processing engine. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. ACM, Austin, p 58

    Google Scholar 

  10. Iosup A, Hegeman T, Ngai WL, Heldens S, Prat A, Manhardt T, Chafi H, Capota 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(12):1317–1328

    Article  Google Scholar 

  11. Kaufmann M, Then M, Kemper A, Neumann T (2017) Parallel array-based single- and multi-source breadth first searches on large dense graphs. In: Proceedings of the 20th International Conference on Extending Database Technology, EDBT Venice, 201721.3.2017. doi:10.5441/002/edbt.2017.02

  12. Klein PN (2005) Multiple-source shortest paths in planar graphs. In: SODA ’05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms, vol 5. ACM, Vancouver, pp 146–155

    Google Scholar 

  13. Kunegis J (2013) Konect: The Koblenz network collection. In: Proceedings of the 22nd international conference on World Wide Web companion. International World Wide Web Conferences Steering Committee, Rio de Janeiro, pp 1343–1350

    Chapter  Google Scholar 

  14. Madduri K, Ediger D, Jiang K, Bader DA, Chavarria-Miranda D (2009) A faster parallel algorithm and efficient multithreaded implementations for evaluating betweenness centrality on massive datasets. In: Parallel & Distributed Processing IEEE International Symposium. IPDPS 2009. IEEE, Rome, pp 1–8

    Google Scholar 

  15. 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 the 2010 ACM SIGMOD International Conference on Management of data. ACM, Indianapolis, pp 135–146

    Chapter  Google Scholar 

  16. Ni C, Sugimoto C, Jiang J (2011) Degree, closeness, and betweenness: Application of group centrality measurements to explore macro-disciplinary evolution diachronically. In: Proceedings of ISSI 2011: The 13th Conference of the International Society for Scientometrics and Informetrics. Durban, pp 1–13

    Google Scholar 

  17. Olsen PW, Labouseur AG, Hwang JH (2014) Efficient top-k closeness centrality search. In: 2014 IEEE 30th International Conference on Data Engineering, pp 196–207

    Chapter  Google Scholar 

  18. Then M, Günnemann S, Kemper A, Neumann T (2017) Efficient batched distance and centrality computation in unweighted and weighted graphs. In: Datenbanksysteme für Business, Technologie und Web (BTW 2017) Stuttgart, 201706.3.2017. 17. Fachtagung des GI-Fachbereichs „Datenbanken und Informationssysteme“ (DBIS). GI, Stuttgart, pp 247–266

  19. Then M, Kaufmann M, Chirigati F, Hoang-Vu TA, Pham K, Kemper A, Neumann T, Vo HT (2014) The more the merrier: efficient multi-source graph traversal. Proc VLDB Endow 8(4):449–460. doi:10.14778/2735496.2735507

    Article  Google Scholar 

  20. Then M, Kersten T, Günnemann S, Kemper A, Neumann T (2017) Automatic algorithm transformation for efficient multi-snapshot analytics on temporal graphs. Proc VLDB Endow 10(8):877–888

    Article  Google Scholar 

  21. Thorup M (2004) Integer priority queues with decrease key in constant time and the single source shortest paths problem. J Comput Syst Sci 69(3):330–353. doi:10.1016/j.jcss.2004.04.003

    Article  MathSciNet  MATH  Google Scholar 

  22. Yen JY (1970) An algorithm for finding shortest routes from all source nodes to a given destination in general networks. Q Appl Math 526–530. doi:10.1090/qam/253822

    MATH  Google Scholar 

Download references

Acknowledgements

This research was supported by the German Research Foundation (DFG), Emmy Noether grant GU 1409/2-1, and by the Technical University of Munich - Institute for Advanced Study, funded by the German Excellence Initiative and the European Union Seventh Framework Programme under grant agreement no 291763, co-funded by the European Union. Manuel Then is a recipient of the Oracle External Research Fellowship. Part of this work was conducted during an internship at Oracle Labs.

Author information

Authors and Affiliations

  1. Department of Informatics, TU Munich, Boltzmannstraße 3, 85748, Garching, Germany

    Manuel Then, Stephan Günnemann, Alfons Kemper & Thomas Neumann

Authors
  1. Manuel Then
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Stephan Günnemann
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alfons Kemper
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Thomas Neumann
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Manuel Then.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Then, M., Günnemann, S., Kemper, A. et al. Efficient Batched Distance, Closeness and Betweenness Centrality Computation in Unweighted and Weighted Graphs. Datenbank Spektrum 17, 169–182 (2017). https://doi.org/10.1007/s13222-017-0261-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13222-017-0261-x

Keywords

Search

Navigation