Dynamic spatio-temporal graph neural networks for hot topic prediction in scientific literature

Abstract

With information explosion occurring in past decades, the rapid growth of papers published results in the rapid change of hot topics, especially in the biomedical domain. It turns out very hard for researchers who are interested in biomedical domain to track hot topics over time, as well as to predict the trends of them in the near future. Based on the above demand, it is important to have a model which is able to follow and predict the trend of hot topics continuously. Deep learning has been proven to be an efficient method to extract information from texts and use the information to predict the future trends. Under the thriving background of Deep Learning, Graph Neural Network (GNN) is able to capture the information from graph structures. There are various applications using GNN models, such as traffic flow prediction, chemical structure discovering, etc. In this research project, a dynamic spatio-temporal graph neural network is presented to keep track of the selected hot keywords and topics in the biomedical domain and predict the possible frequencies in the near future. The input of the model is obtained by extracting the monthly frequency information of selected keywords and topics from paper abstracts in PubMed, the largest biomedical literature collection. After training with data over a decade, the model is able to predict trends of selected hot keywords and topics in next 5 months. Thus, the presented model can help follow the trend of hot topics in the biomedical domain.