Loading...
Please wait, while we are loading the content...
Similar Documents
Understanding Time-Evolving Citation Dynamics across Fields of Sciences
| Content Provider | MDPI |
|---|---|
| Author | Kim, Minkyoung |
| Copyright Year | 2020 |
| Description | Scholarly publications draw collective attention beyond disciplines, leading to highly skewed citation distributions in sciences. Uncovering the mechanisms of such disparate popularity is very challenging, since a wide spectrum of research fields are not only interacting and influencing one another but also time-evolving. Accordingly, this study aims to understand citation dynamics across STEM fields in terms of latent affinity and novelty decay, which is based upon Bayesian inference and learning of the Affinity Poisson Process model (APP) with bibliography data from the Web of Science database. The approaches shown in the study can shed light on predicting and interpreting popularity dynamics in diverse application domains, by considering the effect of time-varying subgroup interactions on diffusion processes. |
| Starting Page | 5846 |
| e-ISSN | 20763417 |
| DOI | 10.3390/app10175846 |
| Journal | Applied Sciences |
| Issue Number | 17 |
| Volume Number | 10 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2020-08-24 |
| Access Restriction | Open |
| Subject Keyword | Applied Sciences Mathematical Social Sciences Statistics and Probability Latent Affinity Interdisciplinary Citations Popularity Dynamics Bayesian Inference |
| Content Type | Text |
| Resource Type | Article |
