We propose a new centrality measure based on a self-adaptive epidemic model characterized by an endogenous reinforcement mechanism in the transmission of information between nodes. We provide a strategy to assign to nodes a centrality score that depends, in an eigenvector centrality scheme, on that of all the elements of the network, nodes and edges, connected to it. We parameterize this score as a function of a reinforcement factor, which for the first time implements the intensity of the interaction between the network of nodes and that of the edges. In this proposal, a local centrality measure representing the steady state of a diffusion process incorporates the global information encoded in the whole network. This measure proves effective in identifying the most influential nodes in the propagation of rumors/shocks/behaviors in a social network. In the context of financial networks, it allows us to highlight strategic assets on correlation networks. The dependence on a coupling factor between graph and line graph also enables the different asset responses in terms of ranking, especially on scale-free networks obtained as minimum spanning trees from correlation networks.

Bartesaghi, P., Clemente, G. P., Grassi, R., A Self-Adaptive Centrality Measure for Asset Correlation Networks, <<ECONOMIES>>, N/A; 12 (7): N/A-N/A. [doi:10.3390/economies12070164] [https://hdl.handle.net/10807/297997]

A Self-Adaptive Centrality Measure for Asset Correlation Networks

Clemente, Gian Paolo;
2024

Abstract

We propose a new centrality measure based on a self-adaptive epidemic model characterized by an endogenous reinforcement mechanism in the transmission of information between nodes. We provide a strategy to assign to nodes a centrality score that depends, in an eigenvector centrality scheme, on that of all the elements of the network, nodes and edges, connected to it. We parameterize this score as a function of a reinforcement factor, which for the first time implements the intensity of the interaction between the network of nodes and that of the edges. In this proposal, a local centrality measure representing the steady state of a diffusion process incorporates the global information encoded in the whole network. This measure proves effective in identifying the most influential nodes in the propagation of rumors/shocks/behaviors in a social network. In the context of financial networks, it allows us to highlight strategic assets on correlation networks. The dependence on a coupling factor between graph and line graph also enables the different asset responses in terms of ranking, especially on scale-free networks obtained as minimum spanning trees from correlation networks.
2024
Inglese
Bartesaghi, P., Clemente, G. P., Grassi, R., A Self-Adaptive Centrality Measure for Asset Correlation Networks, <<ECONOMIES>>, N/A; 12 (7): N/A-N/A. [doi:10.3390/economies12070164] [https://hdl.handle.net/10807/297997]
File in questo prodotto:
File Dimensione Formato  
economies-2024-BartesaghiClementeGrassi.pdf

accesso aperto

Tipologia file ?: Versione Editoriale (PDF)
Licenza: Creative commons
Dimensione 3.67 MB
Formato Adobe PDF
3.67 MB Adobe PDF Visualizza/Apri

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/10807/297997
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact