Benchmarking Graph Databases on the Problem of Community Detection

  • Sotirios Beis
  • Symeon Papadopoulos
  • Yiannis Kompatsiaris
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 312)

Abstract

Thanks to the proliferation of Online Social Networks (OSNs) and Linked Data, graph data have been constantly increasing, reaching massive scales and complexity. Thus, tools to store and manage such data efficiently are absolutely essential. To address this problem, various technologies have been employed, such as relational, object and graph databases. In this paper we present a benchmark that evaluates graph databases with a set of workloads, inspired from OSN mining use case scenarios. In addition to standard network operations, the paper focuses on the problem of community detection and we propose the adaptation of the Louvain method on top of graph databases. The paper reports a comprehensive comparative evaluation between three popular graph databases, Titan, OrientDB and Neo4j. Our experimental results show that in the current development status Neo4j is the most efficient graph database for most of the employed workloads, while Titan handles better single insertion operations.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment 2008(10), P10008 (2008)Google Scholar
  2. 2.
    Giatsoglou, M., Papadopoulos, S., Vakali, A.: Massive graph management for the web and web 2.0. In: Vakali, A., Jain, L.C. (eds.) New Directions in Web Data Management 1. SCI, vol. 331, pp. 19–58. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  3. 3.
    Angles, R., Prat-Pérez, A., Dominguez-Sal, D., Larriba-Pey, J.L.: Benchmarking database systems for social network applications. In: First International Workshop on Graph Data Management Experiences and Systems, GRADES 2013, pp. 15:1–15:7. ACM, New York (2013)Google Scholar
  4. 4.
    Armstrong, T.G., Ponnekanti, V., Borthakur, D., Callaghan, M.: Linkbench: a database benchmark based on the facebook social graph (2013)Google Scholar
  5. 5.
    Grossniklaus, M., Leone, S., Zäschke, T.: Towards a benchmark for graph data management and processing (2013)Google Scholar
  6. 6.
    Vicknair, C., Macias, M., Zhao, Z., Nan, X., Chen, Y., Wilkins, D.: A comparison of a graph database and a relational database: A data provenance perspective. In: Proceedings of the 48th Annual Southeast Regional Conference, ACM SE 2010, pp. 42:1–42:6. ACM, New York (2010)Google Scholar
  7. 7.
    Bader, D.A., Feo, J., Gilbert, J., Kepner, J., Koester, D., Loh, E., Madduri, K., Mann, B., Meuse, T., Robinson, E.: HPC scalable graph analysis benchmark (2009)Google Scholar
  8. 8.
    Dominguez-Sal, D., Urbón-Bayes, P., Giménez-Vañó, A., Gómez-Villamor, S., Martínez-Bazán, N., Larriba-Pey, J.L.: Survey of graph database performance on the hpc scalable graph analysis benchmark. In: Shen, H.T., et al. (eds.) WAIM 2010. LNCS, vol. 6185, pp. 37–48. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  9. 9.
    Ciglan, M., Averbuch, A., Hluchy, L.: Benchmarking traversal operations over graph databases. In: 2012 IEEE 28th International Conference on Data Engineering Workshops (ICDEW), pp. 186–189 (April 2012)Google Scholar
  10. 10.
    Dominguez-Sal, D., Martinez-Bazan, N., Muntes-Mulero, V., Baleta, P., Larriba-Pey, J.: A discussion on the design of graph database benchmarks. In: Nambiar, R., Poess, M. (eds.) TPCTC 2010. LNCS, vol. 6417, pp. 25–40. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  11. 11.
    Jouili, S., Vansteenberghe, V.: An empirical comparison of graph databases. In: 2013 International Conference on Social Computing (SocialCom), pp. 708–715 (September 2013)Google Scholar
  12. 12.
    Dayarathna, M., Suzumura, T.: Xgdbench: A benchmarking platform for graph stores in exascale clouds. In: 2012 IEEE 4th International Conference on Cloud Computing Technology and Science (CloudCom), pp. 363–370 (December 2012)Google Scholar
  13. 13.
    Papadopoulos, S., Kompatsiaris, Y., Vakali, A., Spyridonos, P.: Community detection in social media. Data Mining and Knowledge Discovery 24(3), 515–554 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Sotirios Beis
    • 1
  • Symeon Papadopoulos
    • 1
  • Yiannis Kompatsiaris
    • 1
  1. 1.Information Technologies InstituteCERTHThermiGreece

Personalised recommendations