Time to Cite: Modeling Citation Networks using the Dynamic Impact Single-Event Embedding Model
<|reference_start|>Time to Cite: Modeling Citation Networks using the Dynamic Impact Single-Event Embedding Model: 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.<|reference_end|>
@article{nakis2024time,
    title={Time to Cite: Modeling Citation Networks using the Dynamic Impact
  Single-Event Embedding Model},
    author={Nikolaos Nakis, Abdulkadir Celikkanat, Louis Boucherie, Sune Lehmann,
  Morten M{o}rup},
    journal={arXiv preprint arXiv:2403.00032},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.00032},
    primaryClass={cs.SI cs.AI cs.DL}
}