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Modeling the growth of citation networks is challenging since existing theories of citation are not easy to capture quantitatively and the complex social interactions underlying citation behavior are not well captured by narrowly specified mathematical models. In this respect, agent-based models (ABM) leveraging randomness offer a complementary option. We have previously designed an ABM, implemented in Python, in which agents make citations through a combination of preferential attachment, recency, and fitness. A limitation of this ABM is that it does not scale much beyond networks of a million nodes. We have since developed the Scalable Agent-based Simulator for Citation Analysis with sampling (SASCA-s). Written in C++, SASCA-s uses a refined citation model and scales to over 140 million nodes. We present results from simulations using SASCA-s.

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Very Large Scale Simulations of Network Growth with the Scalable Agent-Based Simulator for Citation Analysis with Sampling (SASCA-s)

  • 会议论文

  • 发表日期：

    2026年

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  • Minhyuk Park,
  • Joǎo A. C. Lamy,
  • Esther C. C. Rodrigues,
  • Felipe M. Ferreira,
  • The-Anh Vu-Le,
  • Tandy Warnow,
  • George Chacko

摘要

Modeling the growth of citation networks is challenging since existing theories of citation are not easy to capture quantitatively and the complex social interactions underlying citation behavior are not well captured by narrowly specified mathematical models. In this respect, agent-based models (ABM) leveraging randomness offer a complementary option. We have previously designed an ABM, implemented in Python, in which agents make citations through a combination of preferential attachment, recency, and fitness. A limitation of this ABM is that it does not scale much beyond networks of a million nodes. We have since developed the Scalable Agent-based Simulator for Citation Analysis with sampling (SASCA-s). Written in C++, SASCA-s uses a refined citation model and scales to over 140 million nodes. We present results from simulations using SASCA-s.