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Networks and Heterogeneous Media

2012, Volume 7, Issue 3: 463-471. doi: 10.3934/nhm.2012.7.463
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Identifying critical traffic jam areas with node centralities interference and robustness

  • 1.
    University of Verona, Center for BioMedical computing, Verona
  • 2.
    University of Verona, Center for BioMedical computing, Department of Pathology, Verona
  • Received: 01 December 2011 Revised: 01 July 2012

  • 05C85, 90C35, 90B10, 90B06.

  • 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.

    Citation: Giovanni Scardoni, Carlo Laudanna. Identifying critical traffic jam areas with node centralitiesinterference and robustness[J]. Networks and Heterogeneous Media, 2012, 7(3): 463-471. doi: 10.3934/nhm.2012.7.463

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  • 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.


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