文献类型：期刊文献

英文题名：Key node identification in social networks based on topological potential model

作者：Zhang, Xueqin[1,2];Wang, Zhineng[1];Liu, Gang[1];Wang, Yan[1]

机构：[1]East China Univ Sci & Technol ECUST, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China;[2]Shanghai Key Lab Comp Software Testing & Evaluatin, Shanghai 201112, Peoples R China

年份：2024

卷号：213

起止页码：158

外文期刊名：COMPUTER COMMUNICATIONS

收录：;EI(收录号：20234615058516);WOS:【SCI-EXPANDED(收录号:WOS:001112507600001)】；

基金：This work is partially supported by the National Natural Science Foundation of China (NSFC No. 51975213) .

语种：英文

外文关键词：Social network; Key node; Information entropy; Effective distance; Topological potential model

摘要：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.