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noisy-networks-measurements

Bayesian reconstruction of networks from noisy measurements, with examples. The theory explaining these models is presented in "Bayesian inference of network structure from unreliable data", by J.-G. Young, G. T. Cantwell and M.E.J. Newman. Here we provide several examples of models coded in Stan, as well as a tutorial reproducing one of the case study of the paper.

Dependencies

The only necessary dependency is stan. The framework will work with any stan interface.

Our tutorial uses the python interface. To install pystan, simply run:

pip install pystan

List of models

We provide code for a several standard models, as well as extensible templates for models not covered by our library of models.

  • Examples: Standard models.
  • Templates: Model templates, that can be used to implement custom models without writing boilerplate code.

Paper

If you use this code, please consider citing:

"Bayesian inference of network structure from unreliable data"
J.-G. Young, G. T. Cantwell and M.E.J. Newman
J. Complex Netw. 8, cnaa046 (2021)

Author information

Code by Jean-Gabriel Young. Don't hesitate to get in touch at jean-gabriel.young@uvm.edu, or via the issues!

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Noisy network measurement with stan

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