As an area of inquiry, the years there has been extensive research in analysis of complex networks primarily driven by the need to understand the nature on information flowing across the network. Influential or critical nodes are the ones responsible for this flow. Therefore, a measurement to find these influential nodes is a critical task. However, these conventional measures either computationally limited or fail to capture the variations in influence. To address this limitation this paper proposes a novel approach by combining Isolating Centrality (ISC) with PageRank, termed Isolating PageRank. The proposed method refines the evaluation of node influence by using ISC to rank the capacity of a node to disconnect the network and supplement it with PageRank’s ability to randomly walk all nodes, thus providing a clearer connectivity significance of a node. Experiments conducted on real world datasets, including fb-pages-public-figures and ca-netscience demonstrate the effectiveness of this approach.