Identification of Influential Nodes in Industrial Networks Based on Structure Analysis

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.