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。

为未来引文预测建立动态异构图和节点重要性模型

Modeling Dynamic Heterogeneous Graph and Node Importance for Future  Citation Prediction

Target-Based Sentiment Analysis aims to detect the opinion aspects (aspect
extraction) and the sentiment polarities (sentiment detection) towards them.
Both the previous pipeline and integrated methods fail to precisely model the
innate connection between these two objectives. In this paper, we propose a
novel dynamic heterogeneous graph to jointly model the two objectives in an
explicit way. Both the ordinary words and sentiment labels are treated as nodes
in the heterogeneous graph, so that the aspect words can interact with the
sentiment information. The graph is initialized with multiple types of
dependencies, and dynamically modified during real-time prediction. Experiments
on the benchmark datasets show that our model outperforms the state-of-the-art
models. Further analysis demonstrates that our model obtains significant
performance gain on the challenging instances under multiple-opinion aspects
and no-opinion aspect situations.

本文提出了一种新的动态异构图结构来联合建模方面提取和情感检测，实验结果表明，我们的模型优于现有最先进的模型，特别在多观点方面和无观点方面的情况下，性能显著提高。