Leveraging neighborhood and path information for influential spreaders recognition in complex networks

The study of influential spreaders has become a growing area of interest within network sciences due to its critical implications in understanding the robustness and vulnerability of complex networks. There is a significant degree of focus on the factors that dictate the decision-making process for identifying these influential spreaders in highly complex networks, given their crucial role in network performance and security. Previous research methodologies have offered a deep understanding of the importance of spreaders, also referred to as nodes. These methods, however, have primarily depended on either neighborhood or path information to identify these spreaders. They have often studied local network data, or adopted a more broad-based, global view of the network. Such an approach may not provide a comprehensive understanding of the overall network structure and the relationships between nodes. Addressing this limitation, our research introduces Neighborhood and Path Information-based Centrality (NPIC) algorithm. This innovative centrality algorithm combines both neighborhood and path information to identify influential spreaders in a complex network. By incorporating these two significant aspects, NPIC provides a more holistic analysis of network centrality, enabling a more accurate identification of influential spreaders. We have subjected NPIC to rigorous testing using numerous simulations on both real and artificially-created datasets. These simulations applied an epidemic model to calculate the spreading efficiency of each node within its given environment. Our simulations, conducted across a wide range of synthetic and real-world datasets, demonstrated that NPIC outperforms existing methodologies in identifying influential spreaders in corresponding networks.