Research on Node Sorting in Scientific Research Cooperation Networks Based on Deep Active Learning
The application of node sorting tasks in social networks and scientific research cooperation is becoming increasingly widespread,and the issue of accurately evaluating the importance of network nodes has attracted much attention.However,cooperative networks often con-tain a large amount of noise,incomplete information,and dynamic changes,and traditional sorting methods often find it difficult to achieve satisfactory results.To this end,a method based on deep active learning is proposed for sorting nodes in scientific research collaboration net-works.This method combines the advantages of deep learning and the query strategy of active learning,and can adaptively sort nodes based on their importance in the network when data labels are scarce and noise interference is high.First utilizes deep learning models to learn represen-tations from the multimodal features of nodes,combining node representations with their importance to form a comprehensive ranking index;Then,active learning methods are used to select nodes that have a significant impact on the ranking results for annotation,gradually optimiz-ing the ranking model.Validation experiments were conducted on real research collaboration network datasets,and it was found that compared with traditional sorting methods,deep active learning based methods have significantly improved accuracy and stability in node sorting.
scientific collaboration networkdeep active learninglearning rankconfidence
万方数据
维普
