The prediction of current scientific impact of papers and authors has been extensively studied to help researchers find valuable papers and recent research directions, also help policymakers make recruitment decisions or funding allocation. However, how to accurately evaluate the future impact of them, especially for new papers and young researchers, is the focus of scientific impact prediction research, and is less explored. Existing graph-based methods heavily depend on the global structure information of heterogeneous academic network and ignore the local structure information and text information, which may provide important clues to identify influential papers and authors with novel perspective. In this paper, we propose a hybrid model called ESMR to predict the future influence of papers and authors by mainly exploiting these information mentioned above. Specifically, we first put forward a novel network embedding-based model, which can capture not only the local structure information, but also the text information of papers into a unified embedding representation. Then, the future impact of papers and authors is mutually ranked by integrating the learned embedding representations into a multivariate random-walk model. Empirical results on two real datasets demonstrate that the proposed method significantly outperforms the existing state-of-the-art ranking methods.

论文和作者当前科学影响的预测已被广泛研究，以帮助研究人员找到有价值的论文和最近的研究方向，也帮助政策制定者做出招聘决定或资金分配。然而，如何准确评估它们的未来影响，尤其是对新论文和年轻研究人员的影响，是科学影响预测研究的重点，探索较少。现有的基于图的方法严重依赖异构学术网络的全局结构信息，而忽略局部结构信息和文本信息，这可能为识别具有新颖观点的有影响力的论文和作者提供重要线索。在本文中，我们提出了一种称为 ESMR 的混合模型，主要利用上述这些信息来预测论文和作者的未来影响力。具体来说，我们首先提出了一种新颖的基于网络嵌入的模型，该模型不仅可以捕获局部结构信息，还可以将论文的文本信息捕获到统一的嵌入表示中。然后，通过将学习到的嵌入表示集成到多元随机游走模型中，对论文和作者的未来影响进行相互排名。两个真实数据集的实证结果表明，所提出的方法明显优于现有的最先进的排名方法。