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Methods for generating complex networks with selected structural properties for simulations: a review and tutorial for neuroscientists

Journal Article


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Abstract


  • Many simulations of networks in computational neuroscience assume completely homogenous random networks of the Erdös–Rényi type, or regular networks, despite it being recognized for some time that anatomical brain networks are more complex in their connectivity and can, for example, exhibit the “scale-free” and “small-world” properties. We review the most well known algorithms for constructing networks with given non-homogeneous statistical properties and provide simple pseudo-code for reproducing such networks in software simulations. We also review some useful mathematical results and approximations associated with the statistics that describe these network models, including degree distribution, average path length, and clustering coefficient. We demonstrate how such results can be used as partial verification and validation of implementations. Finally, we discuss a sometimes overlooked modeling choice that can be crucially important for the properties of simulated networks: that of network directedness.The most well known network algorithms produce undirected networks, and we emphasize this point by highlighting how simple adaptations can instead produce directed networks.

Authors


  •   Prettejohn, Brenton J. (external author)
  •   Berryman, Matthew J.
  •   McDonnell, Mark D. (external author)

Publication Date


  • 2011

Citation


  • Prettejohn, B. J., Berryman, M. J. & McDonnell, M. D. (2011). Methods for generating complex networks with selected structural properties for simulations: a review and tutorial for neuroscientists. Frontiers in Computational Neuroscience, 5 (11), 1-18.

Scopus Eid


  • 2-s2.0-84855427372

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=3498&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/2489

Number Of Pages


  • 17

Start Page


  • 1

End Page


  • 18

Volume


  • 5

Issue


  • 11

Place Of Publication


  • http://www.frontiersin.org/Computational_Neuroscience/10.3389/fncom.2011.00011/abstract

Abstract


  • Many simulations of networks in computational neuroscience assume completely homogenous random networks of the Erdös–Rényi type, or regular networks, despite it being recognized for some time that anatomical brain networks are more complex in their connectivity and can, for example, exhibit the “scale-free” and “small-world” properties. We review the most well known algorithms for constructing networks with given non-homogeneous statistical properties and provide simple pseudo-code for reproducing such networks in software simulations. We also review some useful mathematical results and approximations associated with the statistics that describe these network models, including degree distribution, average path length, and clustering coefficient. We demonstrate how such results can be used as partial verification and validation of implementations. Finally, we discuss a sometimes overlooked modeling choice that can be crucially important for the properties of simulated networks: that of network directedness.The most well known network algorithms produce undirected networks, and we emphasize this point by highlighting how simple adaptations can instead produce directed networks.

Authors


  •   Prettejohn, Brenton J. (external author)
  •   Berryman, Matthew J.
  •   McDonnell, Mark D. (external author)

Publication Date


  • 2011

Citation


  • Prettejohn, B. J., Berryman, M. J. & McDonnell, M. D. (2011). Methods for generating complex networks with selected structural properties for simulations: a review and tutorial for neuroscientists. Frontiers in Computational Neuroscience, 5 (11), 1-18.

Scopus Eid


  • 2-s2.0-84855427372

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=3498&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/2489

Number Of Pages


  • 17

Start Page


  • 1

End Page


  • 18

Volume


  • 5

Issue


  • 11

Place Of Publication


  • http://www.frontiersin.org/Computational_Neuroscience/10.3389/fncom.2011.00011/abstract