Quantifying and predicting the long-term impact of scientific writings or individual scholars has important implications for many policy decisions, such as funding proposal evaluation and identifying emerging research fields. In this work, we propose an approach based on Heterogeneous Dynamical Graph Neural Network (HDGNN) to explicitly model and predict the cumulative impact of papers and authors. HDGNN extends heterogeneous GNNs by incorporating temporally evolving characteristics and capturing both structural properties of attributed graph and the growing sequence of citation behavior. HDGNN is significantly different from previous models in its capability of modeling the node impact in a dynamic manner while taking into account the complex relations among nodes. Experiments conducted on a real citation dataset demonstrate its superior performance of predicting the impact of both papers and authors.

中文翻译：

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一种量化科学影响的异构动态图神经网络方法

量化和预测科学著作或个别学者的长期影响对许多政策决策具有重要意义，例如资助提案评估和确定新兴研究领域。在这项工作中，我们提出了一种基于异构动态图神经网络 (HDGNN) 的方法来显式建模和预测论文和作者的累积影响。HDGNN 通过结合时间演化特征并捕获属性图的结构特性和引用行为的增长序列来扩展异构 GNN。HDGNN 与之前的模型显着不同，它能够以动态方式对节点影响进行建模，同时考虑到节点之间的复杂关系。