Identification of influential vertices plays a very prominent task in a complex network. So, the fundamental task in complex networks is to determine the influential nodes whose expulsion crucially cutoff network harmony. The analysis of the network’s topological characteristics, such as susceptibility and resilience, can be aided by identifying influential nodes. This work uses the notion of Maximize the Number of Connected Components Problem, which helps in determining influential nodes, and whose removal yields optimum number of connected components. This work includes application of topology-based centrality measures on real-world networks. However, conventional methods fail to detect most influential nodes that cause the network to split into the optimum number of components. To address this, our work introduces new centrality called global isolating centrality with half of the diameter hops, that focus on network connectedness. The results reveal that the new centrality is better than the existing centralities for certain probability values.