  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.
