.png){kind=icon}
.png){kind=icon}
Return
An agent-based model of citation behavior
.png){kind=icon}
PRE
AI
DOI：10.1007/s41109-025-00766-z.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Whether citations objectively and reliably reflect the quality of articles and researchers is questionable. Even so, citation counts are widely used to estimate the productivity of researchers and institutions, which creates a 'grubby' motivation to be well-cited. We examine this motivation using a generative model of citation that is agent-based. In this model, new nodes are added to an existing citation network. These new nodes act as autonomous agents that cite other nodes based on a composite bias for preferential attachment, recency, fitness (epistemic quality), and community structure. We use the model to ask whether strategic citation behaviors can support an interest in being well-cited. Results from this model suggest that while fitness is influential, the number of references and community effects are also influential in attracting citations. These results raise questions about similar effects in the real world.
Keywords:
Agents
Mid-level models
Citation networks
Journal
A
IF:
1.5
Papers:
62
Citations:
1.3K
