Data from development and evaluation of SASCA-s: Scalable Agent-based Simulator for Citation Analysis with simulation

The data within consist of compressed output files in the form of edgelists (*.edgelist.gz) and nodelists (*.aux.parquet) from large citation network simulations using an agent-based model. The code and instructions are available at: <a href="https://github.com/illinois-or-research-analytics/SASCA">https://github.com/illinois-or-research-analytics/SASCA</a>. In addition, we provide a distribution of citation frequencies drawn from a random sample of PubMed journal articles (pooled_50k_pubmed_unique.csv) and a table of recencies- the frequency with which citations are made to the previous year, the year before that and so on (recency_probs_percent_stahl_filled.csv). A manuscript describing the SASCA-s simulator has been submitted for review and will be referenced in a future version of this data repository if it is accepted. The prefixes sj and er refer to the real world and Erdos-Renyi random graph respectively that were used to initiate simulations. These 'seed' networks are available from the Github site referenced above.