8 papers with code • 5 benchmarks • 2 datasets
To cope with this problem, we propose a Dynamic heterogeneous Graph and Node Importance network (DGNI) learning framework, which fully leverages the dynamic heterogeneous graph and node importance information to predict future citation trends of newly published papers.
Deep learning has made significant progress in the past decade, and demonstrates potential to solve problems with extensive social impact.
Scientific literature understanding tasks have gained significant attention due to their potential to accelerate scientific discovery.
We show that an informational approach to representing the meaning of a text has offered a way to effectively predict the scientific impact of research papers.
Academic citation graphs represent citation relationships between publications across the full range of academic fields.
We also show the merit of using more training data and longer input for number of citations prediction.
Citation count prediction is the task of predicting the number of citations a paper has gained after a period of time.