Advertisement

Hybrid Centrality Measures for Service Coverage Problem

  • Anuj Singh
  • Rishi Ranjan SinghEmail author
  • S. R. S. Iyengar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11917)

Abstract

Service Coverage Problem aims to find an ideal node for installing a service station in a given network such that services requested from various nodes are satisfied while minimizing the response time. Centrality Measures have been proved to be a salient computational science tool to find important nodes in networks. With increasing complexity and vividness in the network analysis problems, there is a need to modify the existing traditional centrality measures. In this paper we propose a new way of hybridizing centrality measures based on node-weighted centrality measures to address the service coverage problem.

Keywords

Complex network analysis Centrality measures Weighted networks Hybrid centrality 

References

  1. 1.
  2. 2.
    Abbasi, A.: h-Type hybrid centrality measures for weighted networks. Scientometrics 96(2), 633–640 (2013)CrossRefGoogle Scholar
  3. 3.
    Abbasi, A., Hossain, L.: Hybrid centrality measures for binary and weighted networks. In: Menezes, R., Evsukoff, A., González, M. (eds.) Complex Networks. Studies in Computational Intelligence, vol. 424, pp. 1–7. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-30287-9_1CrossRefGoogle Scholar
  4. 4.
    Agarwal, M., Singh, R.R., Chaudhary, S., Iyengar, S.R.S.: An efficient estimation of a node’s betweenness. In: Mangioni, G., Simini, F., Uzzo, S.M., Wang, D. (eds.) Complex Networks VI. SCI, vol. 597, pp. 111–121. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-16112-9_11CrossRefGoogle Scholar
  5. 5.
    Akanmu, A.A., Wang, F.Z., Yamoah, F.A.: Clique structure and node-weighted centrality measures to predict distribution centre location in the supply chain management. In: Science and Information Conference (SAI), pp. 100–111. IEEE (2014)Google Scholar
  6. 6.
  7. 7.
    Batagelj, V., Mrvar, A.: Pajek datasets (2006). http://vlado.fmf.uni-lj.si/pub/networks/data
  8. 8.
    Bonacich, P.: Factoring and weighting approaches to status scores and clique identification. J. Math. Sociol. 2(1), 113–120 (1972)CrossRefGoogle Scholar
  9. 9.
    Brandes, U.: A faster algorithm for betweenness centrality. J. Math. Sociol. 25(2), 163–177 (2001).  https://doi.org/10.1080/0022250X.2001.9990249CrossRefzbMATHGoogle Scholar
  10. 10.
    Brandes, U., Erlebach, T. (eds.): Network Analysis. LNCS, vol. 3418. Springer, Heidelberg (2005).  https://doi.org/10.1007/b106453CrossRefzbMATHGoogle Scholar
  11. 11.
    Brinkhoff, T.: A framework for generating network-based moving objects. GeoInformatica 6(2), 153–180 (2002)CrossRefGoogle Scholar
  12. 12.
    Buechel, B., Buskens, V.: The dynamics of closeness and betweenness. J. Math. Sociol. 37(3), 159–191 (2013)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Buldyrev, S.V., Parshani, R., Paul, G., Stanley, H.E., Havlin, S.: Catastrophic cascade of failures in interdependent networks. Nature 464(7291), 1025–1028 (2010)CrossRefGoogle Scholar
  14. 14.
    Everett, M.G., Borgatti, S.P.: The centrality of groups and classes. J. Math. Sociol. 23(3), 181–201 (1999)CrossRefGoogle Scholar
  15. 15.
    Freeman, L.C.: Centrality in social networks conceptual clarification. Soc. Netw. 1(3), 215–239 (1979)CrossRefGoogle Scholar
  16. 16.
    Jackson, M.O.: Social and Economic Networks. Princeton University Press, Princeton (2008)CrossRefGoogle Scholar
  17. 17.
    Kinney, R., Crucitti, P., Albert, R., Latora, V.: Modeling cascading failures in the north american power grid. Eur. Phys. J. B-Condens. Matter Complex Syst. 46(1), 101–107 (2005)CrossRefGoogle Scholar
  18. 18.
  19. 19.
    Lee, G.S., Djauhari, M.A.: An overall centrality measure: the case of us stock market. Int. J. Electr. Comput. Sci. 12(6) (2012) Google Scholar
  20. 20.
    Leskovec, J., Krevl, A.: SNAP Datasets: Stanford large network dataset collection, June 2014. http://snap.stanford.edu/data
  21. 21.
    Li-Qing, Q., Yong-Quan, L., Zhuo-Yan, C.: A novel algorithm for detecting local community structure based on hybrid centrality. J. Appl. Sci. 14, 3532–3537 (2014)CrossRefGoogle Scholar
  22. 22.
    Opsahl, T., Agneessens, F., Skvoretz, J.: Node centrality in weighted networks: generalizing degree and shortest paths. Soc. Netw. 32(3), 245–251 (2010)CrossRefGoogle Scholar
  23. 23.
    Puzis, R., Elovici, Y., Zilberman, P., Dolev, S., Brandes, U.: Topology manipulations for speeding betweenness centrality computation. J. Complex Netw. 3(1), 84–112 (2014)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Qiao, S., Peng, J., Li, H., Li, T., Liu, L., Li, H.: WebRank: a hybrid page scoring approach based on social network analysis. In: Yu, J., Greco, S., Lingras, P., Wang, G., Skowron, A. (eds.) RSKT 2010. LNCS (LNAI), vol. 6401, pp. 475–482. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-16248-0_67CrossRefGoogle Scholar
  25. 25.
    Qiu, L., Liang, Y., Chen, Z., Fan, J.: A new measurement for the importance of nodes in networks. In: Control Engineering and Information Systems, pp. 483–486 (2014)Google Scholar
  26. 26.
    Rossi, R.A., Ahmed, N.K.: The network data repository with interactive graph analytics and visualization. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (2015). http://networkrepository.com
  27. 27.
    Singh, R.R., Goel, K., Iyengar, S., Gupta, S.: A faster algorithm to update betweenness centrality after node alteration. Internet Math. 11(4–5), 403–420 (2015)MathSciNetCrossRefGoogle Scholar
  28. 28.
    Singh, R.R., Iyengar, S., Chaudhary, S., Agarwal, M.: An efficient heuristic for betweenness estimation and ordering. Soc. Netw. Anal. Mining 8(1), 66 (2018)CrossRefGoogle Scholar
  29. 29.
    Son, S.W., Kim, H., Olave-Rojas, D., lvarez Miranda, E.: Edge information of chilean power grid with tap. Figshare. Dataset (2018)Google Scholar
  30. 30.
    Wang, J., Rong, L., Guo, T.: A new measure of node importance in complex networks with tunable parameters. In: 4th International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2008, pp. 1–4. IEEE (2008)Google Scholar
  31. 31.
    Watts, D.J., Strogatz, S.H.: Collective dynamics of small-world networks. Nature 393(6684), 440–442 (1998)CrossRefGoogle Scholar
  32. 32.
    Wiedermann, M., Donges, J.F., Heitzig, J., Kurths, J.: Node-weighted interacting network measures improve the representation of real-world complex systems. EPL (Europhys. Lett.) 102(2), 28007 (2013)CrossRefGoogle Scholar
  33. 33.
    Zhang, X.J., Wang, Z.L., Zhang, Z.X.: Finding most vital node in satellite communication network. In: Applied Mechanics and Materials, vol. 635, pp. 1136–1139. Trans Tech Publications (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Anuj Singh
    • 1
  • Rishi Ranjan Singh
    • 1
    Email author
  • S. R. S. Iyengar
    • 2
  1. 1.Department of Electrical Engineering and Computer ScienceIndian Institute of TechnologyBhilaiIndia
  2. 2.Department of Computer Science and EngineeringIndian Institute of TechnologyRoparIndia

Personalised recommendations