Key node identification in social networks based on topological potential model

Abstract

With the development of social networks, identifying key nodes in social networks plays a crucial role in preventing and controlling the spread of information. However, the current methods for identifying key nodes have problems such as inaccurate measurement of node value, neglect of mining hidden topology information between nodes, and the calculation of node influence range is not accurate enough etc., and results in low accuracy in identifying key nodes. Aiming at these issues of key node identification, this paper proposes a node influence measurement method based on topological potential model. This method uses information entropy to measure the value of nodes, proposes to apply effective distance to express the relationship between nodes, and evaluates the influence range of each node with the effective distance between the node and its farthest node. Finally, topological potential model is used to measure the importance of nodes, thus improving the accuracy of key node identification. Experiments on multiple datasets show that the proposed method has a better ability to identify key nodes than the compared methods.