Time to Cite: Modeling Citation Networks using the Dynamic Impact Single-Event Embedding Model

Nikolaos Nakis, Abdulkadir Celikkanat, Louis Boucherie, Sune Lehmann, Morten Mørup
Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, PMLR 238:1882-1890, 2024.

Abstract

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 DISEE 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 DISEE well reconciles static latent distance network embedding approaches with classical dynamic impact assessments.

Cite this Paper

BibTeX
@InProceedings{pmlr-v238-nakis24a, title = { Time to Cite: Modeling Citation Networks using the Dynamic Impact Single-Event Embedding Model }, author = {Nakis, Nikolaos and Celikkanat, Abdulkadir and Boucherie, Louis and Lehmann, Sune and M\o{}rup, Morten}, booktitle = {Proceedings of The 27th International Conference on Artificial Intelligence and Statistics}, pages = {1882--1890}, year = {2024}, editor = {Dasgupta, Sanjoy and Mandt, Stephan and Li, Yingzhen}, volume = {238}, series = {Proceedings of Machine Learning Research}, month = {02--04 May}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v238/nakis24a/nakis24a.pdf}, url = {https://proceedings.mlr.press/v238/nakis24a.html}, abstract = { 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 DISEE 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 DISEE well reconciles static latent distance network embedding approaches with classical dynamic impact assessments. } }
Endnote
%0 Conference Paper %T Time to Cite: Modeling Citation Networks using the Dynamic Impact Single-Event Embedding Model %A Nikolaos Nakis %A Abdulkadir Celikkanat %A Louis Boucherie %A Sune Lehmann %A Morten Mørup %B Proceedings of The 27th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2024 %E Sanjoy Dasgupta %E Stephan Mandt %E Yingzhen Li %F pmlr-v238-nakis24a %I PMLR %P 1882--1890 %U https://proceedings.mlr.press/v238/nakis24a.html %V 238 %X 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 DISEE 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 DISEE well reconciles static latent distance network embedding approaches with classical dynamic impact assessments.
APA
Nakis, N., Celikkanat, A., Boucherie, L., Lehmann, S. & Mørup, M.. (2024). Time to Cite: Modeling Citation Networks using the Dynamic Impact Single-Event Embedding Model . Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 238:1882-1890 Available from https://proceedings.mlr.press/v238/nakis24a.html.

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