A Compressed Suffix-Array Strategy for Temporal-Graph Indexing

  • Nieves R. Brisaboa
  • Diego Caro
  • Antonio Fariña
  • M. Andrea Rodríguez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8799)


Temporal graphs represent vertexes and binary relations that change over time. In this paper we consider a temporal graph as a set of 4-tuples ( v s , v e , t s , t e ) indicating that an edge from a vertex v s to a vertex v e is active during the time interval [t s , t e ). Representing those tuples involves the challenge of not only saving space but also of efficient query processing. Queries of interest for these graphs are both direct and reverse neighbors constrained by a time instant or a time interval. We show how to adapt a Compressed Suffix Array (CSA) to represent temporal graphs. The proposed structure, called Temporal Graph CSA (TGCSA), was experimentally compared with a compact data structure based on compressed inverted lists, which can be considered as a fair baseline in the state of the art. Our experimental results are promising. TGCSA obtains a good space-time trade-off, owns wider expressive capabilities than other alternatives, obtains reasonable space usage, and it is efficient even when performing the most complex temporal queries.


Binary Search Query Time Direct Neighbor Neighbor Query Adjacency List 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Albert, R., Barabási, A.L.: Statistical mechanics of complex networks. Rev. Modern Physics 74, 47–97 (2002)CrossRefzbMATHGoogle Scholar
  2. 2.
    Bernardo, G.D., Brisaboa, N.R., Caro, D., Rodriguez, M.A.: Compact Data Structures for Temporal Graphs. In: Proc. DCC 2013, p. 477 (2013)Google Scholar
  3. 3.
    Brisaboa, N., Ladra, S., Navarro, G.: Compact representation of web graphs with extended functionality. Inf. Systems 39(1), 152–174 (2014)CrossRefGoogle Scholar
  4. 4.
    Buin-Xuan, B.M., Ferreira, A., Jarry, A.: Computing shortest, fastest, and foremost journeys in dynamic networks. Int. J. Found. Comput. Sci. 14(02), 267–285 (2003)CrossRefGoogle Scholar
  5. 5.
    Cha, M., Mislove, A., Gummadi, K.P.: A measurement-driven analysis of information propagation in flickr social network. In: Proc. WWW 2009, pp. 721–730 (2009)Google Scholar
  6. 6.
    Fariña, A., Brisaboa, N., Navarro, G., Claude, F., Places, A., Rodríguez, E.: Word-based self-indexes for natural language text. ACM TOIS 30(1), article 1 (2012)Google Scholar
  7. 7.
    Ferreira, A., Viennot, L.: A Note on Models, Algorithms, and Data Structures for Dynamic Communication Networks. Tech. rep., MASCOTTE - INRIA Sophia Antipolis / Laboratoire I3S, HIPERCOM - INRIA Rocquencourt (2002)Google Scholar
  8. 8.
    Khurana, U., Deshpande, A.: Efficient snapshot retrieval over historical graph data. In: Proc. ICDE 2013, pp. 997–1008 (2013)Google Scholar
  9. 9.
    Labouseur, A.G., Birnbaum, J., Olsen, P.W., Spillane, S.R., Vijayan, J., Hwang, J.H., Han, W.S.: The G* graph database: efficiently managing large distributed dynamic graphs. Distributed and Parallel Databases (2014)Google Scholar
  10. 10.
    Labouseur, A.G., Olsen, J.P.W., Hwang, J.H.: Scalable and Robust Management of Dynamic Graph Data. The VLDB Journal, 1–6 (2013)Google Scholar
  11. 11.
    Nicosia, V., Tang, J., Mascolo, C., Musolesi, M., Russo, G., Latora, V.: Graph metrics for temporal networks. In: Temporal Networks, pp. 15–40. Springer (2013)Google Scholar
  12. 12.
    Raman, R., Raman, V., Rao, S.S.: Succinct indexable dictionaries with applications to encoding k-ary trees and multisets. In: Proc. SODA 2012, pp. 233–242 (2002)Google Scholar
  13. 13.
    Ren, C., Lo, E., Kao, B., Zhu, X., Cheng, R.: On querying historical evolving graph sequences. PVLDB 4(11), 726–737 (2011)Google Scholar
  14. 14.
    Sadakane, K.: New text indexing functionalities of the compressed suffix arrays. Journal of Algorithms 48(2), 294–313 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Zhang, J., Long, X., Suel, T.: Performance of compressed inverted list caching in search engines. In: Proc. WWW 2008, pp. 387–396 (2008)Google Scholar
  16. 16.
    Zobel, J., Moffat, A.: Inverted files for text search engines. ACM Computing Surveys 38(2) (July 2006)Google Scholar
  17. 17.
    Zukowski, M., Héman, S., Nes, N., Boncz, P.A.: Super-scalar ram-cpu cache compression. In: Proc. ICDE 2006, p. 59 (2006)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Nieves R. Brisaboa
    • 2
  • Diego Caro
    • 1
  • Antonio Fariña
    • 2
  • M. Andrea Rodríguez
    • 1
  1. 1.Dept. Comp. Sci.University of ConcepciónChile
  2. 2.Database Lab.University of A CoruñaSpain

Personalised recommendations