Skip to main content
SpringerLink
Log in

A Top-Down Scheme for Coverage Centrality Queries on Road Networks

  • Conference paper
  • First Online:
Databases Theory and Applications (ADC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13459))

Included in the following conference series:

Abstract

Coverage Centrality is an important metric to evaluate the node importance in road networks. However, the current solutions have to compute the coverage centrality of all the nodes together, which is resource-wasting especially when only some nodes’ centrality is required. In addition, they have poor adaption to the dynamic scenario because of the computation inefficiency. In this paper, we focus on the coverage centrality query problem and propose an efficient algorithm to compute the centrality of a single node efficiently in both static and dynamic scenarios, with the help of the intra-region pruning, inter-region pruning, and top-down search. Experiments validate the efficiency and effectiveness of our algorithm compared with the state-of-the-art method.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 54.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 69.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Abraham, I., Delling, D., Goldberg, A.V., Werneck, R.F.: A Hub-based labeling algorithm for shortest paths in road networks. In: Pardalos, P.M., Rebennack, S. (eds.) SEA 2011. LNCS, vol. 6630, pp. 230–241. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-20662-7_20

    Chapter  Google Scholar 

  2. Akiba, T., Iwata, Y., Yoshida, Y.: 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, pp. 349–360 (2013)

    Google Scholar 

  3. Brandes, U.: A faster algorithm for betweenness centrality. J. Math. Sociol. 25(2), 163–177 (2001)

    Article  Google Scholar 

  4. Daniel, C., Furno, A., Goglia, L., Zimeo, E.: Fast cluster-based computation of exact betweenness centrality in large graphs. J. Big Data 8 (2021)

    Google Scholar 

  5. De Meo, P., Ferrara, E., Fiumara, G., Provetti, A.: Generalized Louvain method for community detection in large networks. In: 2011 11th International Conference on Intelligent Systems Design and applications, pp. 88–93. IEEE (2011)

    Google Scholar 

  6. Dijkstra, E.W., et al.: A note on two problems in connexion with graphs. Num. Math. 1(1), 269–271 (1959)

    Article  MathSciNet  Google Scholar 

  7. Freeman, L.C.: A set of measures of centrality based on betweenness. Sociometry 40, 35–41 (1977)

    Google Scholar 

  8. Geisberger, R., Sanders, P., Schultes, D., Delling, D.: Contraction hierarchies: faster and simpler hierarchical routing in road networks. In: McGeoch, C.C. (ed.) WEA 2008. LNCS, vol. 5038, pp. 319–333. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-68552-4_24

    Chapter  Google Scholar 

  9. Henry, E., Bonnetain, L., Furno, A., El Faouzi, N.E., Zimeo, E.: Spatio-temporal correlations of betweenness centrality and traffic metrics. In: 2019 6th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), pp. 1–10. IEEE (2019)

    Google Scholar 

  10. Hoang, L., Pontecorvi, M., Dathathri, R., Gill, G., You, B., Pingali, K., Ramachandran, V.: A round-efficient distributed betweenness centrality algorithm. In: Proceedings of the 24th Symposium on Principles and Practice of Parallel Programming, pp. 272–286 (2019)

    Google Scholar 

  11. Ishakian, V., Erdös, D., Terzi, E., Bestavros, A.: A framework for the evaluation and management of network centrality. In: Proceedings of the 2012 SIAM International Conference on Data Mining, pp. 427–438. SIAM (2012)

    Google Scholar 

  12. Jamour, F., Skiadopoulos, S., Kalnis, P.: Parallel algorithm for incremental betweenness centrality on large graphs. IEEE Trans. Parall. Distrib. Syst. 29(3), 659–672 (2017)

    Article  Google Scholar 

  13. Karduni, A., Kermanshah, A., Derrible, S.: A protocol to convert spatial polyline data to network formats and applications to world urban road networks. Sci. Data 3(1), 1–7 (2016)

    Article  Google Scholar 

  14. Kourtellis, N., Morales, G.D.F., Bonchi, F.: Scalable online betweenness centrality in evolving graphs. IEEE Trans. Knowl. Data Eng. 27(9), 2494–2506 (2015)

    Article  Google Scholar 

  15. Lee, M.J., Lee, J., Park, J.Y., Choi, R.H., Chung, C.W.: QUBE: a quick algorithm for updating betweenness centrality. In: Proceedings of the 21st International Conference on World Wide Web, pp. 351–360 (2012)

    Google Scholar 

  16. Li, L., Wang, S., Zhou, X.: Time-dependent hop labeling on road network. In: 2019 IEEE 35th International Conference on Data Engineering (ICDE), pp. 902–913. IEEE (2019)

    Google Scholar 

  17. Li, L., Zhang, M., Hua, W., Zhou, X.: Fast query decomposition for batch shortest path processing in road networks. In: ICDE, pp. 1189–1200. IEEE (2020)

    Google Scholar 

  18. Li, Y., U, L.H., Yiu, M.L., Kou, N.M.: An experimental study on hub labeling based shortest path algorithms. Proc. VLDB Endow. 11(4), 445–457 (2017)

    Google Scholar 

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

    Google Scholar 

  20. Puzis, R., Zilberman, P., Elovici, Y., Dolev, S., Brandes, U.: Heuristics for speeding up betweenness centrality computation. In: 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing, pp. 302–311. IEEE (2012)

    Google Scholar 

  21. Riondato, M., Kornaropoulos, E.M.: Fast approximation of betweenness centrality through sampling. Data Mining Knowl. Discov. 30(2), 438–475 (2015). https://doi.org/10.1007/s10618-015-0423-0

    Article  MathSciNet  MATH  Google Scholar 

  22. Riondato, M., Upfal, E.: ABRA: approximating betweenness centrality in static and dynamic graphs with Rademacher averages. ACM Trans. Knowl. Discov. Data 12(5), 1–38 (2018)

    Article  Google Scholar 

  23. Rupi, F., Bernardi, S., Rossi, G., Danesi, A.: The evaluation of road network vulnerability in mountainous areas: a case study. Netw. Spat. Econ. 15(2), 397–411 (2015)

    Article  MathSciNet  Google Scholar 

  24. Samet, H., Sankaranarayanan, J., Alborzi, H.: Scalable network distance browsing in spatial databases. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, pp. 43–54 (2008)

    Google Scholar 

  25. Sariyüce, A.E., Saule, E., Kaya, K., Çatalyürek, Ü.V.: Shattering and compressing networks for betweenness centrality. In: Proceedings of the 2013 SIAM International Conference on Data Mining, pp. 686–694. SIAM (2013)

    Google Scholar 

  26. Suppa, P., Zimeo, E.: A clustered approach for fast computation of betweenness centrality in social networks. In: 2015 IEEE International Congress on Big Data, pp. 47–54. IEEE (2015)

    Google Scholar 

  27. Yoshida, Y.: Almost linear-time algorithms for adaptive betweenness centrality using hypergraph sketches. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1416–1425 (2014)

    Google Scholar 

  28. Zhang, M., Li, L., Hua, W., Mao, R., Chao, P., Zhou, X.: Dynamic hub labeling for road networks. In: 2021 IEEE 37th International Conference on Data Engineering (ICDE), pp. 336–347. IEEE (2021)

    Google Scholar 

  29. Zhang, M., Li, L., Hua, W., Zhou, X.: Efficient 2-hop labeling maintenance in dynamic small-world networks. In: 2021 IEEE 37th International Conference on Data Engineering (ICDE), pp. 133–144. IEEE (2021)

    Google Scholar 

  30. Zhang, M., Li, L., Zhou, X.: An experimental evaluation and guideline for path finding in weighted dynamic network. Proc. VLDB Endow. 14(11), 2127–2140 (2021)

    Article  Google Scholar 

  31. Zhou, A., Wang, Y., Chen, L.: Butterfly counting on uncertain bipartite graphs. Proc. VLDB Endow. 15(2), 211–223 (2021)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. The Hong Kong University of Science and Technology, Hong Kong, Hong Kong

    Yehong Xu & Lei Li

  2. Iowa State University, Ames, USA

    Mengxuan Zhang

  3. Guangzhou University, Guangzhou, China

    Ruizhong Wu

  4. The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China

    Lei Li

Authors
  1. Yehong Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mengxuan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ruizhong Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lei Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yehong Xu .

Editor information

Editors and Affiliations

  1. The University of Queensland, Brisbane, QLD, Australia

    Wen Hua

  2. Victoria University, Footscray, VIC, Australia

    Hua Wang

  3. The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, China

    Lei Li

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Xu, Y., Zhang, M., Wu, R., Li, L. (2022). A Top-Down Scheme for Coverage Centrality Queries on Road Networks. In: Hua, W., Wang, H., Li, L. (eds) Databases Theory and Applications. ADC 2022. Lecture Notes in Computer Science, vol 13459. Springer, Cham. https://doi.org/10.1007/978-3-031-15512-3_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-15512-3_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-15511-6

  • Online ISBN: 978-3-031-15512-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation