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 assessment measures and the corresponding solutions fail to accurately reveal the global damage done to the network. In order to accurately measure the vulnerability of networks, an invulnerability index based on the concept of improved tenacity is proposed in the present study. Compared with existing measurements, the new method does not measure a single property performance, such as giant component size or the number of components after destruction, but pays special attention to the potential equilibrium between the removal cost and the removal effect. Extensive experiments on real-world social networks demonstrate the accuracy and effectiveness of the proposed method. Moreover, compared with results of attacks based on the different centrality indices, we found an individual node’s prominence in a network is inherently related to the 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. In addition, the experimental results indicate that low centralized networks can tolerate high intentional attacks and has a better adaptability to attacks than high centralized networks.