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Identification of “Die Hard” Nodes in Complex Networks: A Resilience Approach

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Book cover Artificial Life and Evolutionary Computation (WIVACE 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 830))

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Abstract

The topology of a network defines the structure on which physical processes dynamically evolve. Even though the topological analysis of these networks has revealed important properties about their organization, the components of real complex networks can exhibit other significant characteristics. In this work we focus in particular on the distribution of the weights associated to the links. Here, a novel metric is proposed to quantify the importance of both nodes and links in weighted scale-free networks in relation to their resilience. The resilience index takes into account the complete connectivity patterns of each node with all the other nodes in the network and is not correlated with other centrality metrics in heterogeneous weight distributions.

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Author information

Authors and Affiliations

  1. Dipartimento di Ingegneria Elettrica e dell’Informazione, Politecnico di Bari, Bari, Italy

    Angela Lombardi, Angelo Cardellicchio & Cataldo Guaragnella

  2. Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy

    Sabina Tangaro & Roberto Bellotti

  3. Dipartimento Interateneo di Fisica “M. Merlin”, Universitá degli Studi di Bari “A. Moro”, Bari, Italy

    Roberto Bellotti

Authors
  1. Angela Lombardi
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  2. Sabina Tangaro
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  3. Roberto Bellotti
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  4. Angelo Cardellicchio
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  5. Cataldo Guaragnella
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Corresponding author

Correspondence to Angela Lombardi .

Editor information

Editors and Affiliations

  1. Ca’ Foscari University of Venice, Venice, Italy

    Marcello Pelillo

  2. Ca’ Foscari University of Venice, Venice, Italy

    Irene Poli

  3. University of Bologna, Cesena, Italy

    Andrea Roli

  4. University of Modena and Reggio Emilia, Modena, Italy

    Roberto Serra

  5. Ca’ Foscari University of Venice, Venice, Italy

    Debora Slanzi

  6. University of Modena and Reggio Emilia, Modena, Italy

    Marco Villani

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Lombardi, A., Tangaro, S., Bellotti, R., Cardellicchio, A., Guaragnella, C. (2018). Identification of “Die Hard” Nodes in Complex Networks: A Resilience Approach. In: Pelillo, M., Poli, I., Roli, A., Serra, R., Slanzi, D., Villani, M. (eds) Artificial Life and Evolutionary Computation. WIVACE 2017. Communications in Computer and Information Science, vol 830. Springer, Cham. https://doi.org/10.1007/978-3-319-78658-2_19

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  • DOI: https://doi.org/10.1007/978-3-319-78658-2_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-78657-5

  • Online ISBN: 978-3-319-78658-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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