An Agent-based Model of Citation Behavior
Journal:
arXiv
Published Date:
Mar 9, 2025
Abstract
Whether citations can be objectively and reliably used to measure
productivity and scientific quality of articles and researchers can, and
should, be vigorously questioned. However, citations are widely used to
estimate the productivity of researchers and institutions, effectively creating
a 'grubby' motivation to be well-cited. We model citation growth, and this
grubby interest using an agent-based model (ABM) of network growth. In this
model, each new node (article) in a citation network is an autonomous agent
that cites other nodes based on a 'citation personality' consisting of a
composite bias for locality, preferential attachment, recency, and fitness. We
ask whether strategic citation behavior (reference selection) by the author of
a scientific article can boost subsequent citations to it. Our study suggests
that fitness and, to a lesser extent, out_degree and locality effects are
influential in capturing citations, which raises questions about similar
effects in the real world.
