Isolating Coefficient-Based Framework to Recognize Influential Nodes in Complex Networks

Identifying influential nodes within complex networks holds significant importance for enhancing network resilience and understanding vulnerabilities, thereby providing insights for both theoretical exploration and practical applications. Understanding how quickly information spreads highlights the need to identify influential nodes promptly. Certain global centrality measures, including betweenness (BC), closeness (CC), eigenvector (EC), katz centrality, and coreness score (CR), fail to recognize influential nodes situated near the periphery of the network and those with direct connections to the target node. In response to these challenges and to enhance the influence of identification, we have developed a comprehensive framework, namely IS $\mathbb {C}$ , grounded in isolating coefficient, encompassing both a node’s direct connections and those of its neighbors. Using the proposed approach, we defined six new centrality measures, including isolating katz centrality (ISKC), isolating coreness centrality (ISCR), isolating eigenvector centrality (ISEC), isolating betweenness centrality (ISBC), isolating closeness centrality (ISCC), and isolating clustering coefficient centrality (ISCL). Through comparative analysis across five real-world networks, IS $\mathbb {C}$ is evaluated alongside existing centrality measures, confirming its effectiveness in identifying influential nodes while maintaining reasonable computational efficiency. The experimental results affirm that IS $\mathbb {C}$ outperforms other centrality measures in locating influential nodes within complex networks. Additionally, we evaluate the similarity between our proposed methods and both conventional and recent measures by assessing rank correlations using Kendall’s tau coefficient. The simulation outcomes indicate that one of the proposed methods ISCR, utilizing a lower-complexity algorithm, effectively identifies the most influential nodes with high accuracy. Furthermore, statistical techniques were employed to evaluate the proposed methods, demonstrating high reliability and precision in predicting influential nodes across various datasets, as indicated by low standard deviations and narrow confidence intervals.