Name
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s00500-019-04428-y | Soft Computing |
Keywords | DocType | Volume |
Complex networks, Centrality measures, Correlation networks, Relative centrality | Journal | 24 |
Issue | ISSN | Citations |
12 | 1432-7643 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Roy Cerqueti | 1 | 41 | 15.85 |
| Gian Paolo Clemente | 2 | 2 | 3.13 |
| Rosanna Grassi | 3 | 11 | 3.89 |