Advertisement

A New Group Centrality Measure for Maximizing the Connectedness of Network Under Uncertain Connectivity

  • Takayasu Fushimi
  • Kazumi Saito
  • Tetsuo Ikeda
  • Kazuhiro Kazama
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 812)

Abstract

In this paper, we propose a new centrality measure for the purpose of estimating and recommending installation sites of evacuation facilities that many residents can reach even in the situation where roads are blocked by natural disasters. In the proposed centrality, we model the probabilistically occurring road blockage by the link cut of the graph, and quantify the degree of connectivity of each node by the expectation value of the number of reachable nodes under uncertain connectivity. In a large-scale network, since the number of combinations of disconnecting links is enormous, it is difficult to strictly calculate the expected value. Therefore, approximate connectivity is calculated by an efficient algorithm based on simulation. Furthermore, in order to estimate multiple installation sites, we propose a method of defining and maximizing the degree of connectivity for a node group rather than a single node. From this, it can be expected that duplication of nodes covered by the extracted nodes can be eliminated, so that a practical candidate site of evacuation facility can be estimated. We evaluate the effectiveness and efficiency of the proposed method compared with the method based on distance between nodes and the method based on link density, using real road networks.

Keywords

Road network Community extraction Group centrality measure Connectivity 

Notes

Acknowledgement

This work was supported by JSPS Grant-in-Aid for Scientific Research (No.17H01826).

References

  1. 1.
    Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. 2008(10), P10,008 (2008)Google Scholar
  2. 2.
    Chen, P.Y., Hero, A.O.: Deep community detection. IEEE Trans. Signal Process. 63(21), 5706–5719 (2015)Google Scholar
  3. 3.
    Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks. Phys. Rev. E 70(6), 066,111+ (2004).  https://doi.org/10.1103/PhysRevE.70.066111Google Scholar
  4. 4.
    Crucitti, P., Latora, V., Porta, S.: Centrality measures in spatial networks of urban streets. Phys. Rev. E 73(3), 036,125+ (2006)Google Scholar
  5. 5.
    Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. 99(12), 7821–7826 (2002).  https://doi.org/10.1073/pnas.122653799Google Scholar
  6. 6.
    Jain, K., Mahdian, M., Saberi, A.: A new greedy approach for facility location problems. In: Proceedings of the Thiry-Fourth Annual ACM Symposium on Theory of Computing, STOC 2002, pp. 731–740. ACM, New York (2002).  https://doi.org/10.1145/509907.510012
  7. 7.
    Kariv, O., Hakimi, S.L.: An algorithmic approach to network location problems. ii: the p-medians. SIAM J. Appl. Math. 37(3), 539–560 (1979)Google Scholar
  8. 8.
    von Luxburg, U.: A tutorial on spectral clustering. Stat. Comput. 17(4), 395–416 (2007)Google Scholar
  9. 9.
    Montis, D.A., Barthelemy, M., Chessa, A., Vespignani, A.: The structure of interurban traffic: a weighted network analysis. Environ. Plan. B Plan. Des. 34(5), 905–924 (2007)Google Scholar
  10. 10.
    Palla, G., Derényi, I., Farkas, I., Vicsek, T.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435, 814–818 (2005)Google Scholar
  11. 11.
    Park, K., Yilmaz, A.: A social network analysis approach to analyze road networks. In: Proceedings of the ASPRS Annual Conference 2010 (2010)Google Scholar
  12. 12.
    Seidman, S.B.: Network structure and minimum degree. Soc. Netw. 5(3), 269–287 (1983)Google Scholar
  13. 13.
    Tabata, K., Nakamura, A., Kudo, M.: An efficient approximate algorithm for the 1-median problem on a graph. IEICE Trans. Inf. Syst. E100.D(5), 994–1002 (2017).  https://doi.org/10.1587/transinf.2016EDP7398Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Takayasu Fushimi
    • 1
  • Kazumi Saito
    • 2
    • 3
  • Tetsuo Ikeda
    • 4
  • Kazuhiro Kazama
    • 5
  1. 1.School of Computer ScienceTokyo University of TechnologyHachioji-shi, TokyoJapan
  2. 2.Faculty of ScienceKanagawa UniversityHiratsuka-shi, KanagawaJapan
  3. 3.Center for Advanced Intelligence Project, RIKENChuo-ku, TokyoJapan
  4. 4.School of Management and InformationUniversity of ShizuokaSuruga-ku, ShizuokaJapan
  5. 5.Faculty of Systems EngineeringWakayama UniversityWakayama-shi, WakayamaJapan

Personalised recommendations