| Popis: |
Identifying the most important nodes in command and control networks, or evaluating the relative importance of one node compared to others, is a key issue in the study of these networks. Various effective models have been proposed to address this. Traditional gravity models, which consider node degree as the sole factor representing mass, are limited in their ability to accurately identify core nodes, often misclassifying nodes with local high clustering characteristics as central nodes. To improve accuracy, this paper proposes the Coulomb Force Model (CFM), which is inspired by the gravity model but incorporates Coulomb’s law.The CFM uses a dynamic radius method to determine the influence range of each node more precisely. The charge quantity, representing node importance, is determined based on node attributes, and the polarity of the charge indicates the influence on neighboring nodes. This approach introduces intrinsic node information to more accurately measure the differences in node influence, thereby enhancing the algorithm’s precision. We validated the effectiveness and accuracy of the proposed algorithm through comparative experiments on multiple datasets, using classic influence propagation models and evaluation metrics. The results demonstrate the improved performance of the CFM in identifying influential nodes in command and control networks. |
