This package allows to compute the Intrinsic Dimension of unweighted networks using the I3D estimator, contained in the DADApy package(reference here). By means of the PyABC package, we provide an interface to automatically perform a Sequential Monte Carlo Approximate Bayesian Computation (SMCABC) to use the ID signature as a summary statistic in order to infer the parameter of a given generative model that we want to use to model a reference network.
The routines in the package are mainly based on graph-tool, DADApy and PyABC. In order to have everything working properly we suggest to install graph-tool first:
conda create --name gt -c conda-forge graph-tool
conda activate gt
Look here for alternative installations.
Then it you can install PyABC and DADApy through pip:
pip install pyabc
pip install dadapy
If you want to be sure to get the latest releases:
pip install git+https://github.com/icb-dcm/pyabc.git
pip install git+https://github.com/sissa-data-science/DADApy
A couple of routine are based on NetworkX, so we recommend to install it too, always through pip.
Refer to DADApy documentation in order to have mode details about the ID estimator for discrete spaces.
- The module with all the needed functions to generate the simplest graphs
- The module to extract the Intrinsic Dimension and other observables from the graphs
- The module to perform SMC-ABC
- C++ code for ID guided ABC
- Notebook examples:
- ID calculation
- SMC-ABC using a simple model