Seecn: Simulating complex systems using dynamic complex networks

Rick Quax, David A. Bader, Peter M.A. Sloot

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


Multiscale, multiphysics systems are too complex for traditional mathematical modeling and require numerical simulation, yet such systems arise everywhere from modeling the immune system and protein interaction to epidemic spread in a human population. Unfortunately, at present researchers create their own ad hoc programs for their particular study. To address this problem we present the simulator for efficient evolution on complex networks (SEECN), an expressive simulator of complex systems that optimizes for both single-core and parallel performance. In SEECN, a complex network represents the system where the nodes and edges have specified properties that dictate the dynamics of the network over time. Our application is a detailed model of HIV spread among men who have sex with men and serves to show the simulator's expressiveness and to evaluate its performance.

Original languageEnglish (US)
Pages (from-to)201-214
Number of pages14
JournalInternational Journal for Multiscale Computational Engineering
Issue number2
StatePublished - 2011
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computational Mechanics
  • Computer Networks and Communications


  • Complex networks
  • Complex systems
  • Modeling
  • Multiphysics
  • Multiscale
  • Simulation


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