Understanding the structure and dynamics of scientific research, i.e., the science of science (SciSci), has become an important area of research in order to address imminent questions including how scholars interact to advance science, how disciplines are related and evolve, and how research impact can be quantified and predicted. Central to the study of SciSci has been the analysis of citation networks. Here, two prominent modeling methodologies have been employed: one is to assess the citation impact dynamics of papers using parametric distributions, and the other is to embed the citation networks in a latent space optimal for characterizing the static relations between papers in terms of their citations. Interestingly, citation networks are a prominent example of single-event dynamic networks, i.e., networks for which each dyad only has a single event (i.e., the point in time of citation). We presently propose a novel likelihood function for the characterization of such single-event networks. Using this likelihood, we propose the Dynamic Impact Single-Event Embedding model (DISEE). The \textsc{\modelabbrev} model characterizes the scientific interactions in terms of a latent distance model in which random effects account for citation heterogeneity while the time-varying impact is characterized using existing parametric representations for assessment of dynamic impact. We highlight the proposed approach on several real citation networks finding that the DISEE well reconciles static latent distance network embedding approaches with classical dynamic impact assessments.

翻译：理解科学研究的结构及演化规律——即科学学，已成为应对诸多紧迫问题的关键研究领域，包括学者如何通过互动推进科学进步、学科之间如何关联与演变，以及研究影响力如何量化与预测。引文网络分析是科学学研究的核心内容。目前，该领域主要采用两种建模方法论：一是利用参数分布评估论文的引文影响力动态变化，二是将引文网络嵌入到最优表征论文间静态引文关系的潜在空间中。值得注意的是，引文网络是单事件动态网络的典型案例，即每个二元组仅发生一次事件（引文时刻）。本文提出一种适用于此类单事件网络特征刻画的新型似然函数，并基于此构建动态影响力单事件嵌入模型。该模型通过潜在距离模型表征科学互动关系，其中随机效应用于解释引文异质性，而时变影响力则采用现有参数化动态评估方法进行刻画。我们在多个真实引文网络上的实验表明，能有效统一静态潜在距离网络嵌入方法与经典动态影响力评估体系。