Complex network theory enables the analysis and comparison of graphs with a very large number of nodes, or with non-trivial topological properties. Graph models exist for many kinds of networks, ranging from computer networks to representation of protein-protein interactions, and analysis techniques are often shared between fields of application. Infrastructure networks are an active field of application of complex network analysis, which is frequently aimed at finding ways to improve on the structure of a network, while respecting budget constraints. In this activity, complex network analysis is often cross-referenced with simulations or operational research. Power grids stand out among the most prominent examples of infrastructure network analyzed with techniques derived from complex network theory, due to their importance as a service, their properties of quick response to events, and the desired transition to a smart grid paradigm. With the growing interest for the protection of endangered species and habitats, the modeling and analysis of green infrastructure has also received increasing attention from scholars. These classes of infrastructure provide case studies for the exemplification of a common process for the analysis of various kinds of infrastructure networks, which involves the identification of vulnerabilities, the exploration of a search space for possible modifications, and the definition of a comparable measure of health of the network.

Complex network theory enables the analysis and comparison of graphs with a very large number of nodes, or with non-trivial topological properties. Graph models exist for many kinds of networks, ranging from computer networks to representation of protein-protein interactions, and analysis techniques are often shared between fields of application. Infrastructure networks are an active field of application of complex network analysis, which is frequently aimed at finding ways to improve on the structure of a network, while respecting budget constraints. In this activity, complex network analysis is often cross-referenced with simulations or operational research. Power grids stand out among the most prominent examples of infrastructure network analyzed with techniques derived from complex network theory, due to their importance as a service, their properties of quick response to events, and the desired transition to a smart grid paradigm. With the growing interest for the protection of endangered species and habitats, the modeling and analysis of green infrastructure has also received increasing attention from scholars. These classes of infrastructure provide case studies for the exemplification of a common process for the analysis of various kinds of infrastructure networks, which involves the identification of vulnerabilities, the exploration of a search space for possible modifications, and the definition of a comparable measure of health of the network.

Criteria for Modification of Complex Infrastructure Networks

PAU, PIER LUIGI
2017

Abstract

Complex network theory enables the analysis and comparison of graphs with a very large number of nodes, or with non-trivial topological properties. Graph models exist for many kinds of networks, ranging from computer networks to representation of protein-protein interactions, and analysis techniques are often shared between fields of application. Infrastructure networks are an active field of application of complex network analysis, which is frequently aimed at finding ways to improve on the structure of a network, while respecting budget constraints. In this activity, complex network analysis is often cross-referenced with simulations or operational research. Power grids stand out among the most prominent examples of infrastructure network analyzed with techniques derived from complex network theory, due to their importance as a service, their properties of quick response to events, and the desired transition to a smart grid paradigm. With the growing interest for the protection of endangered species and habitats, the modeling and analysis of green infrastructure has also received increasing attention from scholars. These classes of infrastructure provide case studies for the exemplification of a common process for the analysis of various kinds of infrastructure networks, which involves the identification of vulnerabilities, the exploration of a search space for possible modifications, and the definition of a comparable measure of health of the network.
27-mar-2017
Inglese
Complex network theory enables the analysis and comparison of graphs with a very large number of nodes, or with non-trivial topological properties. Graph models exist for many kinds of networks, ranging from computer networks to representation of protein-protein interactions, and analysis techniques are often shared between fields of application. Infrastructure networks are an active field of application of complex network analysis, which is frequently aimed at finding ways to improve on the structure of a network, while respecting budget constraints. In this activity, complex network analysis is often cross-referenced with simulations or operational research. Power grids stand out among the most prominent examples of infrastructure network analyzed with techniques derived from complex network theory, due to their importance as a service, their properties of quick response to events, and the desired transition to a smart grid paradigm. With the growing interest for the protection of endangered species and habitats, the modeling and analysis of green infrastructure has also received increasing attention from scholars. These classes of infrastructure provide case studies for the exemplification of a common process for the analysis of various kinds of infrastructure networks, which involves the identification of vulnerabilities, the exploration of a search space for possible modifications, and the definition of a comparable measure of health of the network.
FENU, GIANNI
Università degli Studi di Cagliari
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14242/69156
Il codice NBN di questa tesi è URN:NBN:IT:UNICA-69156