Modeling Dynamic Heterogeneous Graph and Node Importance for Future Citation Prediction
This addresses the need for editors and readers to quickly identify influential papers, though it is incremental as it builds on existing citation prediction methods.
The paper tackled the problem of predicting future citation counts for newly published papers by proposing a framework that leverages dynamic heterogeneous graph structure and node importance, achieving significant improvements over state-of-the-art models on two large-scale datasets.
Accurate citation count prediction of newly published papers could help editors and readers rapidly figure out the influential papers in the future. Though many approaches are proposed to predict a paper's future citation, most ignore the dynamic heterogeneous graph structure or node importance in academic networks. 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. First, a dynamic heterogeneous network embedding module is provided to capture the dynamic evolutionary trends of the whole academic network. Then, a node importance embedding module is proposed to capture the global consistency relationship to figure out each paper's node importance. Finally, the dynamic evolutionary trend embeddings and node importance embeddings calculated above are combined to jointly predict the future citation counts of each paper, by a log-normal distribution model according to multi-faced paper node representations. Extensive experiments on two large-scale datasets demonstrate that our model significantly improves all indicators compared to the SOTA models.