Towards a More Realistic Citation Model: The Key Role of Research Team Sizes

Abstract

We propose a new citation model which builds on the existing models that explicitly or implicitly include “direct” and “indirect” (learning about a cited paper’s existence from references in another paper) citation mechanisms. Our model departs from the usual, unrealistic assumption of uniform probability of direct citation, in which initial differences in citation arise purely randomly. Instead, we demonstrate that a two-mechanism model in which the probability of direct citation is proportional to the number of authors on a paper (team size) is able to reproduce the empirical citation distributions of articles published in the field of astronomy remarkably well, and at different points in time. Interpretation of our model is that the intrinsic citation capacity, and hence the initial visibility of a paper, will be enhanced when more people are intimately familiar with some work, favoring papers from larger teams. While the intrinsic citation capacity cannot depend only on the team size, our model demonstrates that it must be to some degree correlated with it, and distributed in a similar way, i.e., having a power-law tail. Consequently, our team-size model qualitatively explains the existence of a correlation between the number of citations and the number of authors on a paper.

Universality of citation distributions: A new understanding

“…Such a strategy is known as copying or redirection, although we call it indirect citation. (Our definitions of direct and indirect citations are different from those of Peterson, Presse, and Dill (2010) and Milojevic (2020), who base their models on the preferential attachment mechanism. )…”

Scientific success from the perspective of the strength of weak ties

“…Perhaps this argument could be refuted by using a kind of weighted/directed edge betweenness centrality, which, in combination with the asymmetric overlap introduced in this work, would allow for the formulation of more general laws of social dynamics than those formulated in 31 . An interesting way to overcome this problem has been proposed in 49 , where the authors pointed out that classical betweenness centrality is not useful to measure the influence of a team that is composed of more than two people 50 . Instead of this, a weighted hypergraph representation of the coauthorship network with higher-order interactions has been introduced and betweenness centrality measure has been adequately adapted to this new structure.…”

Uncited papers are not useless

“…where t 0 is the publication year, t is the number of years after publication, N papers (t 0 ) is the number of papers associated with a given discipline that were published in the year t 0 , and N ref erences (t 0 + t) is the total number of references in the papers, belonging to this discipline or community, which were published in the year t 0 + t; A(t) is the aging function for references, namely, the average fraction of references, in the reference list of papers which belong to this discipline and which are t years old (we define it in such a way that ∫ ∞ 0 A(t)dt = 1); η j is the papers' fitness or intrinsic citation capacity (Milojevic, 2020) which captures its potential for garnering citations, in other words, it characterizes the appeal that the paper makes to citing authors after aging and other time-dependent factors have been taken into account. The model assumes that each paper is born with some intrinsic fitness which does not change during the papers' lifetime.…”