Influential nodes recognition of diverse complex network based on deep learning - MA Yulei; GUO Shasha (Dianxin kexue)
To improve the accuracy and robustness of influential node recognition in diverse complex networks, a deep learning-based recognition method for influential nodes in diverse complex networks was proposed. Firstly, multiple centrality indexes were utilized to evaluate the importance of network topology from different perspectives, the weight of each index in different complex networks was decided adaptively through the learnable weight vector. Secondly, a new Transformer framework that could handle features of different dimensions was proposed. Finally, the Transformer model was exployed to realize hierarchical aggregation of the neighbor information in different distances, so as to extract the contextual information of the neighborhood. Validation experiments were carried on multiple complex network datasets, the results showed that the proposed method achieved a good recognition performance of influential nodes for the complex networks of different scales and different categories, effectively improving the accuracy and robustness of influential node recognition.