Multilayer network approach to modeling authorship influence on citation dynamics in physics journals

Abstract

We provide a general framework to model the growth of networks consisting of different coupled layers. Our aim is to estimate the impact of one such layer on the dynamics of the others. As an application, we study a scientometric network, where one layer consists of publications as nodes and citations as links, whereas the second layer represents the authors. This allows us to address the question of how characteristics of authors, such as their number of publications or number of previous coauthors, impacts the citation dynamics of a new publication. To test different hypotheses about this impact, our model combines citation constituents and social constituents in different ways. We then evaluate their performance in reproducing the citation dynamics in nine different physics journals. For this, we develop a general method for statistical parameter estimation and model selection that is applicable to growing multilayer networks. It takes both the parameter errors and the model complexity into account and is computationally efficient and scalable to large networks.