Assessing the Structural Vulnerability of Online Social Networks in Empirical Data - Zhang, Dayong; Guo, Changyong; Zhang, Zhaoxin; Long, Gang (Frontiers in Physics, 2021)
Assessing the structural vulnerability of online social networks has been one of the most engaging topics recently, which is quite essential and beneficial to holding the network connectivity and facilitating information flow, but most of the existing vulnerability assessments measures and the corresponding solutions fail to accurately reveal the global damage done on the network. To measure the vulnerability of networks more properly, we convert the vulnerability assessment into an optimization problem, in which we focus on how to use available resources to most efficiently disrupt network operation. Firstly, we synthetically take account of the cost with which one can disrupt a network and the effect, and present a method based on the concepts of improved tenacity to examine network survivability. Secondly, comparing with results of attacks based on the different centrality indices, we find an individual node’s prominence in a network is inherently related to structural properties of network. In high centralized networks, the nodes with higher eigenvector are more important than the others in maintaining stability and connectivity. But in low centralized networks, the nodes with higher betweenness are more powerful than the others. Moreover, the empirical study reveals that low centralized networks can tolerate high intentional attacks, and has higher anti-jamming capabilities than high centralized networks.