Modeling the global citation network using the scalable agent-based simulator for citation analysis with recency-emphasized sampling (SASCA-ReS)

Abstract The global science literature, represented as a network with articles as nodes and citations as edges, is a rich artifact for scientometric studies. The structure of this network depends on its count of nodes and distribution of edges. We are interested in how the network has evolved from its origin to its present structure. Since extant theories of citation do not offer much in the way of quantitative explanation, we use a modeling approach that generates synthetic networks. Specifically, we have developed an idealized agent-based model of citations (SASCA-ReS) that can generate synthetic networks of size over 200 million nodes, which is comparable with the size of today’s science literature. This model allows us to reason in an artificial world and to identify patterns of citation that may explain real-world scenarios. We report results from simulations under this model with different parameter settings and at various scales to explore counterfactual and hypothetical scenarios.