Package Overview
The main models implemented within the package are found in the GenerativeNetworkModel class.
Additionally, there are four main sub-packages. Below, we give the main functionality of each subpackage. See the full docs pages for the functions and classes available in each sub-package.
model
There are two varieties of models implemented within the package:
- Binary models - these capture only the presence and absence of connections within a network, without capturing their strength
- Weighted models - these additionally capture the strengths of the connections within the network.
Both of these are implemented within the GenerativeNetworkModel class.
gnm.generative_rules
The gnm.generative_rules sub-package contains a collection of different generative rules that can be used to grow and develop the generative network.
gnm.weight_criteria
The gnm.weight_criteria sub-package contains a collection of optimisation criteria that can be used to update the weights of a weighted generative network model.
gnm.evaluation
The gnm.evaluation sub-package contains evaluation criteria which can be used assess the fit between a set of synthetic and real networks.
gnm.fitting
The gnm.fitting sub-package contains methods to fit parameters to a dataset and perform sweeps over parameters.
gnm.defaults
The gnm.defaults sub-package contains default values that can be used to run experiments out-of-the-box
gnm.utils
Finally, the gnm.utils sub-package contains other useful functionality for working with the toolbox, including functionality for computing statistics and various graph measures