Skip to main content
Log in

Answering Reachability Queries on Incrementally Updated Graphs by Hierarchical Labeling Schema

  • Regular Paper
  • Published:
Journal of Computer Science and Technology Aims and scope Submit manuscript

Abstract

We proposed a novel solution schema called the Hierarchical Labeling Schema (HLS) to answer reachability queries in directed graphs. Different from many existing approaches that focus on static directed acyclic graphs (DAGs), our schema focuses on directed cyclic graphs (DCGs) where vertices or arcs could be added to a graph incrementally. Unlike many of the traditional approaches, HLS does not require the graph to be acyclic in constructing its index. Therefore, it could, in fact, be applied to both DAGs and DCGs. When vertices or arcs are added to a graph, the HLS is capable of updating the index incrementally instead of re-computing the index from the scratch each time, making it more efficient than many other approaches in the practice. The basic idea of HLS is to create a tree for each vertex in a graph and link the trees together so that whenever two vertices are given, we can immediately know whether there is a path between them by referring to the appropriate trees. We conducted extensive experiments on both real-world datasets and synthesized datasets. We compared the performance of HLS, in terms of index construction time, query processing time and space consumption, with two state-of-the-art methodologies, the path-tree method and the 3-hop method. We also conducted simulations to model the situation when a graph is updated incrementally. The performance comparison of different algorithms against HLS on static graphs has also been studied. Our results show that HLS is highly competitive in the practice and is particularly useful in the cases where the graphs are updated frequently.

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

Access this article

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

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Yan X, Yu P, Han J. Substructure similarity search in graph databases. In Proc. the 2005 ACM SIGMOD International Conference on Management of Data, June 2005, pp.766-777.

  2. Golumbic M. Algorithm Graph Theory and Perfect Graphs (2nd edition). Amsterdam, The Netherlands: North-Holland, 2004.

  3. Tian Y, Hankins R, Patel J. Efficient aggregation for graph summarization. In Proc. the 2008 ACM SIGMOD International Conference on Management of Data, June 2008, pp.567-580.

  4. Jin R, Xiang Y, Ruan N, Wang H. Efficiently answering reachability queries on very large directed graphs. In Proc. the 2008 ACM SIGMOD International Conference on Management of Data, June 2008, pp.595-608.

  5. Jin R, Xiang Y, Ruan N, Fuhry D. 3HOP: A highcompression indexing scheme for reachability query. In Proc. the 2009 SIGMOD International Conference on Management of Data, June 29-July 2, 2009, pp.813-826.

  6. Wu Y, Huang H, Hao Z, Chen F. Local community detection using link similarity. Journal of Computer Science and Technology, 2012, 27(6): 1261–1268.

    Article  Google Scholar 

  7. Zhang Q. Dynamic uncertain causality graph for knowledge representation and reasoning: Discrete DAG cases. Journal of Computer Science and Technology, 2012, 27(1): 1–23.

    Article  MathSciNet  MATH  Google Scholar 

  8. Pechsiri C, Piriyakul R. Explanation knowledge graph construction through causality extraction from texts. Journal of Computer Science and Technology, 2010, 25(5): 1055–1070.

    Article  Google Scholar 

  9. D’Ulizia A, Ferri F, Formica A, Grifoni P. Approximating geographical queries. Journal of Computer Science and Technology, 2009, 24(6): 1109–1124.

    Article  Google Scholar 

  10. Sakr S, Al-Naymat G. Efficient relational techniques for processing graph queries. Journal of Computer Science and Technology, 2010, 25(6): 1237–1255.

    Article  Google Scholar 

  11. Abed S, Mohamed O A, Al-Sammane G. An abstract reachability approach by combining HOL induction and multiway decision graphs. Journal of Computer Science and Technology, 2009, 24(1): 76–95.

    Article  MathSciNet  Google Scholar 

  12. Jagadish H. A compression technique to materialize transitive closure. ACM Transactions on Database Systems (TODS), 1990, 15(4): 558–598.

    Article  MathSciNet  Google Scholar 

  13. Chen Y, Chen Y. An efficient algorithm for answering graph reachability queries. In Proc. the 24th IEEE International Conference on Data Engineering, April 2008, pp.893-902.

  14. Wang H, He H, Yang J, Yu P, Yu J. Dual labeling: Answering graph reachability queries in constant time. In Proc. the 22nd IEEE International Conference on Data Engineering, April 2006, pp.75:1–75:12.

  15. Agrawal R, Borgida A, Jagadish H. Efficient management of transitive relationships in large data and knowledge bases. In Proc. the 1989 ACM SIGMOD International Conference on Management of Data, May 31-June 2, 1989, pp.253-262.

  16. Chen L, Gupta A, Kurul M. Stack-based algorithms for pattern matching on DAGs. In Proc. the 31st International Conference on Very Large Data Bases, August 30- September 2, 2005, pp.493-504.

  17. Dey S K, Jamil H. A hierarchical approach to reachability query answering in very large graph databases. In Proc. the 19th ACM International Conference on Information and Knowledge Management, Oct. 2010, pp.1377-1380.

  18. Triβl S, Leser U. Fast and practical indexing and querying of very large graphs. In Proc. the 2007 ACM SIGMOD International Conference on Management of Data, June 2007, pp.845-856.

  19. Cohen E, Halperin E, Kaplan H, Zwick U. Reachability and distance queries via 2-hop labels. In Proc. the 13th Annual ACM-SIAM Symposium on Discrete Algorithms, January 2002, pp.937-946.

  20. Schenkel R, Theobald A, Weikum G. HOPI: An efficient connection index for complex XML document collections. In Proc. the 9th Advances in Database Technology-EDBT 2004, March 2004, pp.237-255.

  21. Cheng J, Yu J, Lin X, Wang H, Yu P. Fast computation of reachability labeling for large graphs. In Proc. the 10th Advances in Database Technology-EDBT 2006, March 2006, pp.961-979.

  22. Cheng J, Yu J, Lin X,Wang H, Yu P. Fast computing reachability labelings for large graphs with high compression rate. In Proc. the 11th International Conference on Extending Database Technology: Advances in Database Technology, March 2008, pp.193-204.

  23. Demetrescu C, Italiano G F. Fully dynamic all pairs shortest paths with real edge weights. Journal of Computer and System Sciences, 2006, 72(5): 813–837.

    Article  MathSciNet  MATH  Google Scholar 

  24. Henzinger M R, King V. Fully dynamic biconnectivity and transitive closure. In Proc. the 36th Annual Symposium on Foundations of Computer Science, October 1995, pp.664- 672.

  25. Roditty L. Decremental maintenance of strongly connected components. In Proc. the 24th Annual ACM-SIAM Symposium on Discrete Algorithms, January 2013, pp.1143-1150.

  26. Roditty L, Zwick U. A fully dynamic reachability algorithm for directed graphs with an almost linear update time. In Proc. the 36th Annual ACM Symposium on Theory of Computing, June 2004, pp.184-191.

  27. Bramandia R, Choi B, Ng W K. Incremental maintenance of 2-hop labeling of large graphs. IEEE Transactions on Knowledge and Data Engineering, 2010, 22(5): 682–698.

    Article  Google Scholar 

  28. Schenkel R, Theobald A, Weikum G. Efficient creation and incremental maintenance of the HOPI index for complex XML document collections. In Proc. the 21st IEEE International Conference on Data Engineering, April 2005, pp.360-371.

  29. Jin R, Ruan N, Xiang Y, Wang H. Path-tree: An efficient reachability indexing scheme for large directed graphs. ACM Transactions on Database Systems (TODS), 2011, 36(1): 7:1–7:44.

  30. Yildirim H, Chaoji V, Zaki M J. DAGGER: A scalable index for reachability queries in large dynamic graphs. http://arxiv.org/abs/1301.0977, Jan. 2016.

  31. Zhu A D, Lin W, Wang S, Xiao X. Reachability queries on large dynamic graphs: A total order approach. In Proc. the 2014 ACM SIGMOD International Conference on Management of Data, June 2014, pp.1323-1334.

  32. Krommidas I, Zaroliagis C. An experimental study of algorithms for fully dynamic transitive closure. Journal of Experimental Algorithmics (JEA), 2008, 12: 1.6:1–1.6:22.

  33. Cheng J, Huang S, Wu H, Fu A W C. TF-label: A topological-folding labeling scheme for reachability querying in a large graph. In Proc. the 2013 ACM SIGMOD International Conference on Management of Data, June 2013, pp.193-204.

  34. Jin R, Wang G. Simple, fast, and scalable reachability oracle. Proc. the VLDB Endowment, 2013, 6(14): 1978–1989.

    Article  MathSciNet  Google Scholar 

  35. Yano Y, Akiba T, Iwata Y, Yoshida Y. Fast and scalable reachability queries on graphs by pruned labeling with landmarks and paths. In Proc. the 22nd ACM International Conference on Information and Knowledge Management, October 27-November 1, 2013, pp.1601-1606.

  36. Yıdırım H, Chaoji V, Zaki M J. GRAIL: A scalable index for reachability queries in very large graphs. The VLDB Journal, 2012, 21(4): 509–534.

    Article  Google Scholar 

  37. Johnsonbaugh R, Kalin M. A graph generation software package. ACM SIGCSE Bulletin, 1991, 23(1): 151–154.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Tak-Lam Wong.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wong, TL. Answering Reachability Queries on Incrementally Updated Graphs by Hierarchical Labeling Schema. J. Comput. Sci. Technol. 31, 381–399 (2016). https://doi.org/10.1007/s11390-016-1633-7

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11390-016-1633-7

Keywords

Navigation