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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.

In complex networks, node impact refers to an individual node’s significance or influence within the structure. The evaluation of the impact of the nodes in information transmission, prevention of pandemics, and resilience applications of the infrastructure is studied. Centrality measures are crucial for understanding the impact of particular nodes in the network structure. Most centrality measures, such as degree centrality, betweenness centrality, and eigenvector centrality, provide influential node information based on network aspects such as connection patterns, paths for communication, and influence propagation dynamics. However, these centrality measures could capture local and global information by balancing time complexity and spreading efficiency. This paper proposes an Isolating-Betweenness Centrality (ISBC) for quantifying node impact by incorporating the properties Betweenness Centrality and Isolating Centrality. The proposed measure evaluates a node’s impact by considering local and global structural influence. We verify the SIR and IC epidemic models to evaluate ISBC’s performance compared with conventional and recent centrality measures on real-world datasets. Furthermore, we show that the proposed measure exhibits improved spreading efficiency over recent and conventional measures with moderate time complexity.

Exploring the significance of popular node’s impact in complex networks yields numerous advantages, such as improving network resilience and accelerating information dissemination. While conventional centrality measures accurately quantify individual node importance, they may inadvertently overlook certain properties of influential nodes. The quest for new centrality metrics has garnered substantial research due to their theoretical relevance and practical applicability in real-world network scenarios. The existing research has predominantly focused on designing centrality metrics based on the local and/or global topological characteristics of nodes. Nevertheless, these metrics do not consider the nodes located in the intermediary zones between the inner and outer regions of a network, resulting in reduced effectiveness when applied to large-scale network scenarios. To address these challenges, we have introduced a novel convex framework to formulate the Convex Mixed Centrality (COMC) measure. This metric aims to overcome the limitations of traditional centrality metrics by incorporating insights from both local and global network dynamics, thus enhancing its ability to identify influential nodes across various network regions. To prove the efficacy of our proposed measure, we utilize the Susceptible-Infected-Recovered (SIR) and Independent Cascade (IC) models, alongside the Kendall tau metric. Extensive simulation experiments conducted on various real-world datasets demonstrate that the COMC measure outperforms conventional centrality indices in terms of spreading efficiency, all while maintaining comparable computational complexity.

The detection and ranking of influential nodes in complex networks are crucial for various practical applications such as identifying potential drug targets in protein-to-protein interaction networks, critical devices in communication networks, key people in social networks, and transportation hubs in logistics networks. The knowledge of influential spreaders in complex networks is extremely useful for controlling the spread of information. Centrality measures are known for effectively quantifying the influential nodes information in large-scale complex networks. Researchers have proposed different centrality measures in the literature, including Degree, Betweenness, Closeness, and Clustering coefficient centralities. However, these measures have certain limitations when implemented over large-scale complex networks. Most of these measures can be classified as global and local structural approaches. The global structure based algorithms are too complex to evaluate key nodes, particularly in large-scale networks, whereas the local measures overlook the essential global network information. To address these challenges, an extended clustering coefficient local global centrality (ECLGC) is proposed, which combines the local and global structural information to measure the node’s influence in large-scale networks. The effectiveness and computational efficiency of the proposed measure are compared with existing centrality measures on real-world network datasets. Susceptible-Infected-Recovered (SIR) model is utilized to evaluate the performance of the ECLGC to capture the high-information dissemination compared to conventional measures. Further, we demonstrate that the proposed measure outperforms the conventional measures in terms of spreading efficiency.

Over the past decade, there has been extensive research conducted on complex networks, primarily driven by their crucial role in understanding the various real-world networks such as social networks, communication networks, transportation networks, and biological networks. Ranking influential nodes is one of the fundamental research problems in the areas of rumor spreading, disease research, viral marketing, and drug development. Influential nodes in any network are used to disseminate the information as fast as possible. Centrality measures are designed to quantify the node’s significance and rank the influential nodes in complex networks. However, these measures typically focus on either the local or global topological structure within and outside network communities. In particular, many measures limit their ability to capture the node’s overall impact on small-scale networks. To address these challenges, we develop a novel centrality measure called Isolating Clustering Distance Centrality (ICDC) by integrating the isolating and clustering coefficient centrality measures. The proposed metric gives a more thorough assessment of the node’s importance by integrating the local isolation and global topological influence in large-scale complex networks. We employ the SIR and ICM epidemic models to study the efficiency of ICDC against traditional centrality measures across real-world complex networks. Our experimental findings consistently highlight the superior efficacy of ICDC in terms of fast spreading and computational efficiency when compared to existing centrality measures.