Identification of Influential Nodes in Industrial Networks Based on Structure Analysis
Open Access
- 22 January 2022
- Vol. 14 (2), 211
- https://doi.org/10.3390/sym14020211
Abstract
Industrial network systems are facing various new challenges, such as increasing functional failure factors, the accelerating penetration of information threats, and complex and diverse attack methods. Industrial networks are often vulnerable to natural or intentional disasters; therefore, it is highly invaluable to research to identify the influential nodes. Most of the state-of-the-art evaluates the importance of the nodes according to one or more network metrics. Moreover, there are no metrics reflecting all the properties of the network. In this paper, a novel method (Structure-based Identification Method, SIM) to identify the influential nodes in industrial networks is proposed based on the network structure, which goes beyond the use of network metrics. The SIM method extracts the weakly connected components, which are more likely to survive after the important nodes are attacked in the network. Evaluation results show that the SIM method obtains better results than the state-of-the-art methods to identify influential nodes in real-world industrial networks and has a good prospect to be applied in industrial application.Keywords
Funding Information
- Ministry of Science and Technology (2020YFB2009500)
- State Key Laboratory of Robotics (2022-Z13)
This publication has 22 references indexed in Scilit:
- Robustness of airline alliance route networksCommunications in Nonlinear Science and Numerical Simulation, 2015
- Percolation of localized attack on complex networksNew Journal of Physics, 2015
- Mitigation of malicious attacks on networksProceedings of the National Academy of Sciences of the United States of America, 2011
- EFFECT OF ATTACK ON SCALE-FREE NETWORKS DUE TO CASCADING FAILUREModern Physics Letters B, 2009
- Collaborative attack on Internet users' anonymityInternet Research, 2009
- Fast unfolding of communities in large networksJournal of Statistical Mechanics: Theory and Experiment, 2008
- A faster algorithm for betweenness centrality*The Journal of Mathematical Sociology, 2001
- The number of cut-vertices in a graph of given minimum degreeDiscrete Mathematics, 1991
- A Set of Measures of Centrality Based on BetweennessSociometry, 1977
- Communication Patterns in Task-Oriented GroupsThe Journal of the Acoustical Society of America, 1950