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.

提出了一种基于动态异构图和节点重要性网络学习框架的方法来预测新发表论文的未来引用趋势，采用动态异构网络嵌入模块、节点重要性嵌入模块和对数正态分布来进行多面的论文节点表示和联合预测，通过实验证实该方法显著优于目前最先进的方法。