Eur. Phys. J. Special Topics 228, 2475-2490 (2019)
https://doi.org/10.1140/epjst/e2019-800196-2

Regular Article

Detecting the significant nodes in two-layer flow networks: an interlayer non-failure cascading effect perspective

¹ School of Economics and Management, China University of Geosciences, Beijing 100083, P.R. China
² Key Laboratory of Carrying Capacity Assessment for Resource and Environment, Ministry of Natural Resources, Beijing 100083, P.R. China
³ School of Information Engineering, China University of Geosciences, Beijing 100083, P.R. China

^a e-mail: gxy5669777@126.com

Received: 11 November 2018
Received in final form: 6 December 2018
Published online: 28 October 2019

Abstract

Detecting the significant nodes in multilayer networks is crucial for preventing the large-scale spread of disaster events. However, the existing model can hardly simulate the ubiquitous non-failure cascading effect process in social and economic systems. To solve this problem, first, we propose a mathematical method of constructing a two-layer network model. Then we define the non-failure cascading effect process in the two-layer network. Based on the model and spreading process, we propose a non-failure cascading effect index by using each node's non-failure cascading affecting in uential degree on the two-layer network. We then applied the detecting model in theoretical two-layer networks. We find there exist significant nodes, and also exist several in uential factors of the interlayer cascading effect process. The detecting model is applied in the two-layer industrial input{ output networks between the U.S. and China for testing the validity of the theoretical model. The hybrid network combination is relatively more sensitive to in uential factors; the significant nodes are more prominent in scale-free networks. Our research provides a solution for finding the significant nodes in two-layer social or economic networks based on the non-failure cascading effect process.