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.

翻译：精确预测新发表论文的未来引用次数，有助于编辑和读者快速识别未来具有影响力的论文。尽管已有多种方法被提出用于预测论文的未来引用，但大多忽略了学术网络中的动态异质图结构或节点重要性。为解决这一问题，我们提出了一种动态异质图与节点重要性网络（DGNI）学习框架，该框架充分利用动态异质图和节点重要性信息来预测新发表论文的未来引用趋势。首先，提供动态异质网络嵌入模块以捕捉整个学术网络的动态演化趋势；其次，提出节点重要性嵌入模块，通过捕捉全局一致性关系来确定每篇论文的节点重要性；最后，将上述计算得到的动态演化趋势嵌入和节点重要性嵌入相结合，基于多侧面论文节点表示所构建的对数正态分布模型，共同预测每篇论文的未来引用次数。在两个大规模数据集上的广泛实验表明，与现有最优模型相比，本模型在所有指标上均取得了显著提升。