Robustness Analysis of Complex Networks under Set Centrality Attacks

Many everyday human activities, such as socialization, information gathering, transportation, emergency communication, and healthcare, depend heavily on communication networks. The failure of even a few key components of such systems can be dramatic, potentially disconnecting millions of users. Therefore, the analysis of networks' robustness has become a key area of research in computer science. Robustness can be defined as the ability of a network to continue to perform well when it is subject to failures, such as fiber cuts, configuration errors, viruses and worms, cyberattacks, terrorism, or natural disasters. In this work, we simulate several attacks on complex networks by targeting the most central nodes. Centrality metrics, such as betweenness, are widely used to identify nodes that act as critical bridges in the flow of information. To efficiently identify these high-impact nodes, we employ CentRA, a specialized algorithm designed to pinpoint the most central set of nodes within a network. An evaluation of the network's overall performance is carried out by analyzing various robustness metrics. This analysis allows us to quantify the vulnerability of the network to targeted disruptions and better understand the structural properties that contribute to its resilience.