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International Journal for Multiscale Computational Engineering
IF: 1.016 5-Year IF: 1.194 SJR: 0.452 SNIP: 0.68 CiteScore™: 1.18

ISSN Print: 1543-1649
ISSN Online: 1940-4352

International Journal for Multiscale Computational Engineering

DOI: 10.1615/IntJMultCompEng.v9.i2.50
pages 201-214

SEECN: SIMULATING COMPLEX SYSTEMS USING DYNAMIC COMPLEX NETWORKS

Rick Quax
Computational Science, University of Amsterdam, Netherlands
David A. Bader
College of Computing, Georgia Institute of Technology, USA
Peter M. A. Sloot
Computational Science, University of Amsterdam, Netherlands

ABSTRACT

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.

REFERENCES

  1. Barabasi, A.-L. and Albert, R., Emergence of scaling in random networks. DOI: 10.1126/science.286.5439.509

  2. Barrett, C. L., Bisset, K. R., Eubank, S. G., Feng, X., and Marathe, M. V., Episimdemics: An efficient algorithm for simulating the spread of infectious disease over large realistic social networks. DOI: 10.1109/SC.2008.5214892

  3. Colizza, V., Barrat, A., Barthelemy, M., and Vespignani, A., Predictability and epidemic pathways in global outbreaks of infectious diseases: The SARS case study. DOI: 10.1186/1741-7015-5-34

  4. Deissenberg, C., van der Hoog, S., and Dawid, H., EURACE: A massively parallel agent-based model of the European economy. DOI: 10.1016/j.amc.2008.05.116

  5. Leskovec, J., Chakrabarti, D., Kleinberg, J., and Faloutsos, C., Realistic, mathematically tractable graph generation and evolution, using Kronecker multiplication. DOI: 10.1007/11564126_17

  6. Macal, C. M. and North, M. J., Tutorial on agent-based modeling and simulation. DOI: 10.1109/WSC.2005.1574234

  7. Madduri, K., A High-Performance Framework for Analyzing Massive Complex Networks. DOI: 1853/24712

  8. Mahdian, M. and Xu, Y., Stochastic Kronecker graphs. DOI: 10.1007/978-3-540-77004-6_14

  9. Minar, N., Burkhart, R., Langton, C., and Askenazi, M., The Swarm simulation system: A toolkit for building multi-agent simulations.

  10. Nagel, K., Beckman, R. L., and Barrett, C. L., TRANSIMS for transportation planning.

  11. NetworkWorkbench Tool Team (NWB Team), Network Workbench Tool.

  12. Nikolai, C. and Madey, G., Tools of the trade: A survey of various agent based modeling platforms.

  13. North, M. J., Howe, T. R., Collier, N. T., and Vos, J. R., The Repast Simphony runtime system.

  14. North, M. J., Macal, C. M., Pieper, G. W., and Drugan, C. G., Agent-based modeling and simulation for EXASCALE computing.

  15. Quax, R., Modeling and simulating the propagation of infectious diseases using complex networks. DOI: 1853/24827

  16. Railsback, S. F., Lytinen, S. L., and Jackson, S. K., Agent-based simulation platforms: Review and development recommendations. DOI: 10.1177/0037549706073695

  17. Ravasz, E. and Barab´asi, A.-L., Hierarchical organization in complex networks. DOI: 10.1103/PhysRevE.67.026112

  18. Saad, Y., Krylov subspace methods on supercomputers. DOI: 10.1137/0910073

  19. Schneeberger, A., Mercer, C. H., Gregson, S. A., Ferguson, N. M., Nyamukapa, C. A., Anderson, R. M., Johnson, A. M., and Garnett, G. P., Scale-free networks and sexually transmitted diseases: A description of observed patterns of sexual contacts in Britain and Zimbabwe. DOI: 10.1097/00007435-200406000-00012

  20. Socioeconomic Data and Applications Center (SEDAC), Gridded population of the world, version 3 (GPWv3) and the global rural-urban mapping project (GRUMP).