Advanced Search
Article Contents
Article Contents

Identifying critical traffic jam areas with node centralities interference and robustness

Abstract Related Papers Cited by
  • 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.
    Mathematics Subject Classification: 05C85, 90C35, 90B10, 90B06.


    \begin{equation} \\ \end{equation}
  • [1]

    R. Albert, H. Jeong and A.-L. Barabási, Error and attack tolerance of complex networks, Nature, 406 (2000), 378-382.


    A.-L. Barabási and R. Albert, Emergence of scaling in random networks, Science, 286 (1999), 509-512.


    A.-L. Barabási and Z. N. Oltvai, Network biology: Understanding the cell's functional organization, Nature Reviews Genetics, 5 (2004), 101-113.


    U. S. Bhalla and R. Iyengar, Emergent properties of networks of biological signaling pathways, Science, 283 (1999).


    G. Caldarelli, "Scale-Free Networks: Complex Webs in Nature and Technology (Oxford Finance)," Oxford University Press, USA, June 2007.


    P. Crucitti, V. Latora, M. Marchiori and A. Rapisarda, Error and attack tolerance of complex networks, News and expectations in thermostatistics, Phys. A, 340 (2004), 388-394.


    J. A. Goguen and J. Meseguer, Security policies and security models, Symposium on Security and Privacy, IEEE Computer Society Press, (1982), 11-20.


    H. Jeong, S. P. Mason, A. L. Barabási and Z. N. Oltvai, Lethality and centrality in protein networks, Nature, 411 (2001), 41-42.


    H. Jeong, B. Tombor, R. Albert, Z. N. Oltvai and A. L. Barabási, The large-scale organization of metabolic networks, Nature, 407 (2000), 651-654.


    D. Koschützki, K. A. Lehmann, L. Peeters, S. Richter, D. T. Podehl and O. Zlotowski, Centrality indices, in "Network Analysis: Methodological Foundations" (eds. U. Brandes and T. Erlebach), Springer, (2005), 16-61.


    R. Milo, S. Shen-Orr, S. Itzkovitz, N. Kashtan, D. Chklovskii and U. Alon, Network motifs: Simple building blocks of complex networks, Science, 298 (2002), 824-827.


    M. E. J. Newman, Modularity and community structure in networks, Proceedings of the National Academy of Sciences, 103 (2006), 8577-8582.


    The official, Autostrade per l'Italia, http://www.autostrade.it/, 2011.


    G. Scardoni, M. Petterlini and C. Laudanna, Analyzing biological network parameters with CentiScaPe, Bioinformatics, 25 (2009), 2857-2859.


    C. M. Schneider, T. Mihaljev, S. Havlin and H. J. Herrmann, Suppressing epidemics with a limited amount of immunization units, Physical Review E, 84 (2011), 061911+.


    S. H. Strogatz, Exploring complex networks, Nature, 410 (2001), 268-276.


    Duncan J. Watts and Steven H. Strogatz, Collective dynamics of 'small-world' networks, Nature, 393 (1998), 440-442.

  • 加载中

Article Metrics

HTML views() PDF downloads(113) Cited by(0)

Access History

Other Articles By Authors



    DownLoad:  Full-Size Img  PowerPoint