Enhanced complex network influential node detection through the integration of entropy and degree metrics with node distance

Complex networks play a vital role in various real-world systems, including marketing, information dissemination, transportation, biological systems, and epidemic modeling. Identifying influential nodes within these networks is essential for optimizing spreading processes, controlling rumors, and preventing disease outbreaks. However, existing state-of-the-art methods for identifying influential nodes face notable limitations. For instance, Degree Centrality (DC) measures fail to account for global information, the K-shell method does not assign a unique ranking to nodes, and global measures are often computationally intensive. To overcome these challenges, this paper proposes a novel approach called Entropy Degree Distance Combination (EDDC), which integrates both local and global measures, such as degree, entropy, and distance. This approach incorporates local structure information by using entropy as a local metric and enhances the understanding of the overall graph structure by including path information as part of the global measure. This innovative method makes a substantial contribution to various applications, including virus spread modeling, viral marketing etc. The proposed approach is evaluated on six different benchmark datasets using well-known evaluation metrics and proved its efficiency.