The key contribution of this paper is to embed the analysis of the network in a framework based on a mapping from the input space whose elements are nodes of a graph or the entire graph into an information space whose elements are probability distributions associated to objects in the input space. Specifically, a node is associated to the probability distribution of its node‐to‐node distances and the whole graph to the aggregation of these node distributions. In this space two distances are proposed for this analysis: Jensen‐Shannon and Wasserstein, based respectively on information theory and optimal transport theory. This representation allows to compute the distance between the original network and the one obtained by the removal of nodes or edges and use this distance as an index of the increase in vulnerability induced by the removal. In this way a new characterization of vulnerability is obtained. This new index has been tested in two real‐world water distribution networks. The results obtained are discussed along those which relate vulnerability to the loss of efficiency and those given by the analysis of the spectra of the adjacency and Laplacian matrices of the network. The models and algorithms considered in this paper have been integrated into an analytics framework which can also support the analysis of other networked infrastructures among which power grids, gas distribution, and transit networks are included.

Ponti, A., Candelieri, A., Giordani, I., Archetti, F. (2021). A novel graph‐based vulnerability metric in urban network infrastructures: The case of water distribution networks. WATER, 13(11) [10.3390/w13111502].

A novel graph‐based vulnerability metric in urban network infrastructures: The case of water distribution networks

Candelieri, A;Giordani, I;Archetti, F
2021

Abstract

The key contribution of this paper is to embed the analysis of the network in a framework based on a mapping from the input space whose elements are nodes of a graph or the entire graph into an information space whose elements are probability distributions associated to objects in the input space. Specifically, a node is associated to the probability distribution of its node‐to‐node distances and the whole graph to the aggregation of these node distributions. In this space two distances are proposed for this analysis: Jensen‐Shannon and Wasserstein, based respectively on information theory and optimal transport theory. This representation allows to compute the distance between the original network and the one obtained by the removal of nodes or edges and use this distance as an index of the increase in vulnerability induced by the removal. In this way a new characterization of vulnerability is obtained. This new index has been tested in two real‐world water distribution networks. The results obtained are discussed along those which relate vulnerability to the loss of efficiency and those given by the analysis of the spectra of the adjacency and Laplacian matrices of the network. The models and algorithms considered in this paper have been integrated into an analytics framework which can also support the analysis of other networked infrastructures among which power grids, gas distribution, and transit networks are included.
Articolo in rivista - Articolo scientifico
Jensen‐Shannon divergence; Network analysis; Robustness; Spectral analysis; Wasserstein distance; Water distribution network;
English
2021
Ponti, A., Candelieri, A., Giordani, I., Archetti, F. (2021). A novel graph‐based vulnerability metric in urban network infrastructures: The case of water distribution networks. WATER, 13(11) [10.3390/w13111502].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/324182
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