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Modeling and simulations of the cascading failure of multiple interdependent R&D networks under risk propagation


Liu, Hui; Yang, Naiding; Yang, Zhao; Zhang, Yanlu; Lin, Jianhong (2019). Modeling and simulations of the cascading failure of multiple interdependent R&D networks under risk propagation. Physics Letters A, 383(21):2443-2450.

Abstract

In this paper, we study the robustness of multiple interrelated R&D networks under risk propagation. Firstly, using a bi-partite graph to represent the interrelated R&D networks is emphasized and proposed. Secondly, a risk propagation model is built by defining risk load and risk capacity of each enterprise on a specific R&D network, Thirdly, we use simulations to study risk propagation in interrelated R&D networks. Our results indicate that there exist three critical thresholds to quantify the robustness of R&D networks. Risk propagation in R&D networks is highly affected by the heterogeneity of all enterprises' scales and risk capacities.

Abstract

In this paper, we study the robustness of multiple interrelated R&D networks under risk propagation. Firstly, using a bi-partite graph to represent the interrelated R&D networks is emphasized and proposed. Secondly, a risk propagation model is built by defining risk load and risk capacity of each enterprise on a specific R&D network, Thirdly, we use simulations to study risk propagation in interrelated R&D networks. Our results indicate that there exist three critical thresholds to quantify the robustness of R&D networks. Risk propagation in R&D networks is highly affected by the heterogeneity of all enterprises' scales and risk capacities.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Business Administration
03 Faculty of Economics > Department of Informatics
08 Research Priority Programs > Social Networks
Dewey Decimal Classification:330 Economics
Scopus Subject Areas:Physical Sciences > General Physics and Astronomy
Language:English
Date:29 July 2019
Deposited On:10 Oct 2019 12:52
Last Modified:12 Jan 2021 04:36
Publisher:Elsevier
ISSN:0375-9601
OA Status:Closed
Publisher DOI:https://doi.org/10.1016/j.physleta.2019.05.012
Other Identification Number:merlin-id:18632

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