Influence measures in subnetworks using vertex centrality

Gian Paolo Clemente, Roy Cerqueti, Rosanna Grassi

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

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.
Original languageEnglish
Pages (from-to)8569-8582
Number of pages14
JournalSoft Computing
DOIs
Publication statusPublished - 2019

Keywords

  • Centrality measures
  • Complex networks
  • Correlation networks
  • Relative centrality

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