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.

Identification of “Die Hard” nodes in complex networks: A resilience approach / Lombardi, Angela; Tangaro, Sabina; Bellotti, Roberto; Cardellicchio, Angelo; Guaragnella, Cataldo. - STAMPA. - 830:(2018), pp. 257-268. (Intervento presentato al convegno 12th Italian Workshop on Artificial Life and Evolutionary Computation, WIVACE 2017 tenutosi a Venezia, Italy nel September 19-21, 2017) [10.1007/978-3-319-78658-2_19].

Identification of “Die Hard” nodes in complex networks: A resilience approach

Lombardi, Angela;Cardellicchio, Angelo;Guaragnella, Cataldo
2018-01-01

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.
2018
12th Italian Workshop on Artificial Life and Evolutionary Computation, WIVACE 2017
978-3-319-78657-5
https://link.springer.com/chapter/10.1007%2F978-3-319-78658-2_19
Identification of “Die Hard” nodes in complex networks: A resilience approach / Lombardi, Angela; Tangaro, Sabina; Bellotti, Roberto; Cardellicchio, Angelo; Guaragnella, Cataldo. - STAMPA. - 830:(2018), pp. 257-268. (Intervento presentato al convegno 12th Italian Workshop on Artificial Life and Evolutionary Computation, WIVACE 2017 tenutosi a Venezia, Italy nel September 19-21, 2017) [10.1007/978-3-319-78658-2_19].
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11589/138715
Citazioni
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact