Identifying Influential Nodes Based on Optimized Structural Holes in Complex Networks

• Published in: 2022 7th International Conference on Computational Intelligence and Applications (ICCIA) pp. 219 - 224
• Main Authors: Liu, Rui, Zhang, Sheng, Mei, Wanting, Zhang, Donghui, Sun, Ka
• Format: Conference Proceeding
• Language: English
• Published: IEEE 24.06.2022
• Subjects: Complex networks; Computational intelligence; Computational modeling; Correlation; influential nodes; similarity; structural holes; susceptibility-infection model
• DOI: 10.1109/ICCIA55271.2022.9828453

The influential nodes identification has been an interesting problem in this issue. Aiming at the existing influential nodes identification methods that only consider the number of neighbors of the node, the topological relationship between the node and the neighbor is ignored, resulting in a one-sided problem. Proposing the influential nodes identification based on optimized structural holes. This method uses the similarity between nodes to distinguish the differences of neighboring nodes to optimize the constraint. The optimized constraint reflects the number of neighbors of the node and the topological structure between nodes and neighbors. The propagation ability of the influential nodes identified by the proposed method is verified on the SI (susceptible-infection) model, and compared with other methods, it is found that the proposed method is effective and feasible for the identification of influential nodes.