Influence maximization algorithm of social networks based on Transformer model

The network topology structure based influence maximization algorithms are greatly influenced by the network structure, which leads to unstable performance of social networks of different scales and different topology structures. In view of this problem, a improved Transformer model based social network influence maximization algorithm was proposed. Firstly, the high influential nodes of the society network were selected based on the k-shell decomposition method. Seconcly, the topology structure information and connection framework information of the candidate nodes were discovered by use of the random walk strategy. Finally, the Transformer model was improved, in order to support scalable node feature sequences, and the improved Transformer model was taken advantage to predict the seed nodes of the social network. Validation experiments were carried on six real social networks of different scales. The results show that the proposed algorithm realizes a good influence maximization performance on social networks of different scales and topology structures, and the time efficiency of the seed node recognition has been increased significantly.