Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/329090 
Year of Publication: 
2024
Citation: 
[Journal:] Economies [ISSN:] 2227-7099 [Volume:] 12 [Issue:] 7 [Article No.:] 164 [Year:] 2024 [Pages:] 1-19
Publisher: 
MDPI, Basel
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.
Subjects: 
centrality measures
eigenvector centrality
epidemic models
nonlinear eigenproblem
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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