Influence measures in subnetworks using vertex centrality
Cerqueti, R., Clemente, G.P. and Grassi, R. (2019). Influence measures in subnetworks using vertex centrality. Soft Computing. https://doi.org/10.1007/s00500-019-04428-y
|Authors||Cerqueti, R., Clemente, G.P. and Grassi, R.|
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.
This is a post-peer-review, pre-copyedit version of an article published in Soft Computing. The final authenticated version is available online at: http://dx.doi.org/10.1007/s00500-019-04428-y.
|Digital Object Identifier (DOI)||https://doi.org/10.1007/s00500-019-04428-y|
|Online||25 Oct 2019|
|Publication process dates|
|Accepted||25 Aug 2019|
|Deposited||11 Feb 2020|
|Accepted author manuscript|
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