This work deals with the issue of assessing the influence of a node in the entire network and in the subnetwork to which it belongs as well, adapting the classical idea of vertex centrality. We provide a general definition of relative vertex centrality measure with respect to the classical one, referred to the whole network. Specifically, we give a decomposition of the relative centrality measure by including also the relative influence of the single node with respect to a given subgraph containing it. The proposed measure of relative centrality is tested in the empirical networks generated by collecting assets of the S&P 100, focusing on two specific centrality indices: betweenness and eigenvector centrality. The analysis is performed in a time perspective, capturing the assets influence, with respect to the characteristics of the analysed measures, in both the entire network and the specific sectors to which the assets belong.
Cerqueti, R., Clemente, G., & Grassi, R. (2020). Influence measures in subnetworks using vertex centrality. SOFT COMPUTING, 24(12), 8569-8582.
|Citazione:||Cerqueti, R., Clemente, G., & Grassi, R. (2020). Influence measures in subnetworks using vertex centrality. SOFT COMPUTING, 24(12), 8569-8582.|
|Tipo:||Articolo in rivista - Articolo scientifico|
|Carattere della pubblicazione:||Scientifica|
|Presenza di un coautore afferente ad Istituzioni straniere:||No|
|Titolo:||Influence measures in subnetworks using vertex centrality|
|Autori:||Cerqueti, R; Clemente, G; Grassi, R|
|Data di pubblicazione:||2020|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1007/s00500-019-04428-y|
|Appare nelle tipologie:||01 - Articolo su rivista|