Identifying Critical Road Network Areas with Node Centralities Interference and Robustness

  • Giovanni Scardoni
  • Carlo Laudanna
Part of the Studies in Computational Intelligence book series (SCI, volume 424)


We introduce the notions of centrality interference and centrality robustness, as measures of variation of centrality values when the structure of a network is modified by removing or adding individual nodes from/to a network. Centrality analysis allows categorizing nodes according to their topological relevance in a network. Thus, centrality interference analysis allows understanding which parts of a network are mostly influenced by a node and, conversely, centrality robustness allows quantifying the functional dependency of a node from other nodes in the network. We examine the theoretical significance of these measures and apply them to classify nodes in a road network to predict the effects on the traffic jam due to variations in the structure of the network. In these case the interference analysis allows to predict which are the distinct regions of the network affected by the function of different nodes. Such notions, when applied to a variety of different contexts, opens new perspectives in network analysis since they allow predicting the effects of local network modifications on single node as well as global network functionality.


Short Path Road Network Single Node Entire Network Node Removal 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Caldarelli, G.: Scale-Free Networks: Complex Webs in Nature and Technology (Oxford Finance). Oxford University Press, USA (June 2007)Google Scholar
  2. 2.
    Bhalla, U.S., Iyengar, R.: Emergent properties of networks of biological signaling pathways. Science 283 (January 1999)Google Scholar
  3. 3.
    Strogatz, S.H.: Exploring complex networks. Nature 410(6825), 268–276 (2001)CrossRefGoogle Scholar
  4. 4.
    Barabási, A.-L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Jeong, H., Tombor, B., Albert, R., Oltvai, Z.N., Barabási, A.L.: The large-scale organization of metabolic networks. Nature 407(6804), 651–654 (2000)CrossRefGoogle Scholar
  6. 6.
    Newman, M.E.J.: Modularity and community structure in networks. Proceedings of the National Academy of Sciences 103(23), 8577–8582 (2006)CrossRefGoogle Scholar
  7. 7.
    Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., Alon, U.: Network motifs: Simple building blocks of complex networks. Science 298(5594), 824–827 (2002)CrossRefGoogle Scholar
  8. 8.
    Watts, D.J., Strogatz, S.H.: Collective dynamics of ’small-world’ networks. Nature 393(6684), 440–442 (1998)CrossRefGoogle Scholar
  9. 9.
    Koschützki, D., Lehmann, K.A., Peeters, L., Richter, S., Podehl, D.T., Zlotowski, O.: Centrality indices. In: Brandes, U., Erlebach, T. (eds.) Network Analysis: Methodological Foundations, pp. 16–61. Springer (2005)Google Scholar
  10. 10.
    Barabási, A.-L., Oltvai, Z.N.: Network biology: understanding the cell’s functional organization. Nature Reviews Genetics 5(2), 101–113 (2004)CrossRefGoogle Scholar
  11. 11.
    Jeong, H., Mason, S.P., Barabási, A.L., Oltvai, Z.N.: Lethality and centrality in protein networks. Nature 411(6833), 41–42 (2001)CrossRefGoogle Scholar
  12. 12.
    Albert, R., Jeong, H., Barabási, A.-L.: Error and attack tolerance of complex networks. Nature 406(6794), 378–382 (2000)CrossRefGoogle Scholar
  13. 13.
    McCulloh, I., Carley, K.: Detecting change in longitudinal social networks. Journal of Social Structure 12 (2011)Google Scholar
  14. 14.
    Crucitti, P., Latora, V., Marchiori, M., Rapisarda, A.: Error and attack tolerance of complex networks. Physica A: Statistical Mechanics and its Applications 340(1-3), 388 (2004); News and Expectations in ThermostatisticsMathSciNetCrossRefGoogle Scholar
  15. 15.
    Liu, Y.-Y., Slotine, J.-J., Barabási, A.-L.: Controllability of complex networks. Nature 473(7346), 167–173 (2011)CrossRefGoogle Scholar
  16. 16.
    Freeman, L.C.: Centrality in social networks: conceptual clarification. Social Networks 1, 215–239 (1978)CrossRefGoogle Scholar
  17. 17.
    Scardoni, G., Petterlini, M., Laudanna, C.: Analyzing biological network parameters with CentiScaPe. Bioinformatics 25(21), 2857–2859 (2009)CrossRefGoogle Scholar
  18. 18.
    Goguen, J.A., Meseguer, J.: Security policies and security models. In: 1982 Symposium on Security and Privacy, pp. 11–20. IEEE Computer Society Press (1982)Google Scholar
  19. 19.
    The official ”autostrade per l’Italia” website (2011),

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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Center for BioMedical Computing (CBMC)University of VeronaVeronaItaly
  2. 2.Department of PathologyUniversity of VeronaVeronaItaly

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