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Title: An influential node identification method considering multi-attribute decision fusion and dependency
Abstract

It is essential to study the robustness and centrality of interdependent networks for building reliable interdependent systems. Here, we consider a nonlinear load-capacity cascading failure model on interdependent networks, where the initial load distribution is not random, as usually assumed, but determined by the influence of each node in the interdependent network. The node influence is measured by an automated entropy-weighted multi-attribute algorithm that takes into account both different centrality measures of nodes and the interdependence of node pairs, then averaging for not only the node itself but also its nearest neighbors and next-nearest neighbors. The resilience of interdependent networks under such a more practical and accurate setting is thoroughly investigated for various network parameters, as well as how nodes from different layers are coupled and the corresponding coupling strength. The results thereby can help better monitoring interdependent systems.

Authors:
; ; ;
Publication Date:
NSF-PAR ID:
10380328
Journal Name:
Scientific Reports
Volume:
12
Issue:
1
ISSN:
2045-2322
Publisher:
Nature Publishing Group
Sponsoring Org:
National Science Foundation
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