Advertisement

A Shortest Path Query Method Based on Tree Decomposition and Label Coverage

  • Xiaohuan Shan
  • Xin Wang
  • Jun Pang
  • Liyan Jiang
  • Baoyan SongEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9998)

Abstract

The shortest path query is one of core contents in graph theory study, various problems in the real world can be transformed into it to solve. With the increase of network scale, classic shortest path query algorithms cannot meet the query demand on large-scale graphs by reason of query efficiency, storage costs, etc. In order to solve above problems, we lucubrate on previous works, and propose a novel method based on tree decomposition and label coverage (TDLC-SP) which consists of two phases: offline pretreatment phase and online query phase. In the pretreatment phase, we propose a novel acceleration index method TDLC, it maps the graph into a tree, allocates minimum label coverage for each vertex to reduce redundant data storage and vertices traversal range; In the query phase, utilizing the TDLC index, query is completed by traversing the tree structure only once, it further improves the query efficiency. Experimental results on several real-world networks and synthetic datasets demonstrate the efficiency and effectiveness of the proposed methods.

Keywords

Shortest path query Large graph Pretreatment Tree decomposition Label coverage 

Notes

Acknowledgements

This work was supported by National Natural Science Foundation of China under Grant (Nos. 61472169, 61502215); Science Research Normal Fund of Liaoning Province Education Department (No. L2015193); Doctoral Scientic Research Start Foundation of Liaoning Province (No. 201501127); Young Research Foundation of Liaoning University under Grant (No. LDQN201438).

References

  1. 1.
    Tong, Y.X., She, J.Y., Meng, R.: Bottleneck-aware arrangement over event-based social networks: the max-min approach. World Wide Web-internet Web Inf. Syst. 19, 1–27 (2015)Google Scholar
  2. 2.
    Tong, Y.X., She, J.Y., Chen, L.: Towards better understanding of app functions. J. Comput. Sci. Technol. 30(5), 1130–1140 (2015)MathSciNetCrossRefGoogle Scholar
  3. 3.
    She, J.Y, Tong, Y.X., Chen, L.: Utility-aware event-participant planning. In: Proceedings of the 34th ACM SIGMOD International Conference on Management of Data (SIGMOD 2015), Melbourne, Victoria, Australia, pp. 1629–1643 (2015)Google Scholar
  4. 4.
    Dijkstra, E.W.: A note on two problems in connexion with graphs. J. Numer. Math. 1(1), 269–271 (1959)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Floyd, R.W.: Algorithm 97: shortest path. Commun. ACM 5(6), 345–348 (1962)CrossRefGoogle Scholar
  6. 6.
    Xiao, Y.H., Wu, W.T., Pei, J., et al.: Efficiently indexing shortest paths by exploiting symmetry in graphs. In: Proceedings of the 12th International Conference on Extending Database Technology, Saint Petersburg, Russia, pp. 493–504 (2009)Google Scholar
  7. 7.
    Takuya, A., Christian, S., et al.: Shortest-path queries for complex networks: exploiting low tree-width outside the core. In: Proceedings of the 15th International Conference on Extending Database Technology, New York, USA, pp. 144–155 (2012)Google Scholar
  8. 8.
    Goldberg, A.V., Werneck, R.: Computing point-to-point shortest paths from external memory. In: Proceedings of the 7th Workshop on Algorithm Engineering and Experiments, London, pp. 26–40 (2005)Google Scholar
  9. 9.
    Schultes, D.: Route planning in road networks, Ph.D. thesis, Universitat Karlsruhe (2008)Google Scholar
  10. 10.
    Maue, J., Sanders, P., et al.: Goal directed shortest path queries using precomputed cluster distances. J. Exp. Algorithms 14(32), 1–27 (2009). ACMMathSciNetzbMATHGoogle Scholar
  11. 11.
    Fang, W.: TEDI: efficient shortest path query answering on graphs. In: Proceedings of the 2010 ACM SIGMOD International Conference on Management of data, pp. 99–110 (2010)Google Scholar
  12. 12.
    Konstantin, T., Abel, A.-C., et al.: Fast fully dynamic landmark-based estimation of shortest path distances in very large graphs. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, vol. 278(13), pp. 1785–1794 (2011)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Xiaohuan Shan
    • 1
  • Xin Wang
    • 1
  • Jun Pang
    • 2
  • Liyan Jiang
    • 1
  • Baoyan Song
    • 1
    Email author
  1. 1.School of InformationLiaoning UniversityShenyangChina
  2. 2.School of Information Science and EngineeringNortheastern UniversityShenyangChina

Personalised recommendations