BETA RELEASE: This is a beta release currently being tested by users. Your feedback is valuable as we work towards finalizing the package!
Welcome to the documentation for the Probabilistic Inference on Networks
(ProbINet) Python
package. This project is a
collaborative effort to consolidate state-of-the-art probabilistic generative modeling implementations from various
scientific publications. Our focus lies in advancing network analysis techniques with an emphasis on recent modeling
approaches that relax the restrictive conditional independence assumption, enabling the modeling of joint
distributions of network data.
The ProbINet package is designed to be a comprehensive and user-friendly toolset for
researchers and practitioners
interested in modeling network data through probabilistic generative approaches. Our goal is to provide a
unified resource that brings together different advances scattered across many code repositories.
By doing so, we aim not only to enhance the usability of existing models, but also to facilitate the comparison
of different approaches. Moreover, through a range of tutorials, we aim at simplifying the use of these methods
to perform inferential tasks, including the prediction of missing network edges, node clustering (community detection),
anomaly identification, and the generation of synthetic data from latent variables.
This package requires Python 3.10. Please ensure you have this version before proceeding with the installation.
To get started, follow these steps:
- Create a virtual environment. For example, using
venv:
python3.10 -m venv --copies venv
. venv/bin/activate
(venv) pip install -U pip # optional but always advised!- Install the
ProbINetpackage by running:
(venv) pip install .Run the ProbINet package as a whole with the run_model command. A list of the parameters that can be passed as arguments is available by running:
run_model --helpTo run a specific model, pass the model name as an argument. The available models are: CRep, JointCRep, MTCOV, DynCRep, and ACD. For example, to run the CRep model, use:
run_model -a CRepTo see the specific options for a model, use the -h flag. For example, to see the options for the CRep model, use:
run_model CRep -hThe run_model command can be run at different logging levels. To run the command with the DEBUG level, use:
run_model -a CRep -dTo run the tests:
python -m unittestThe documentation can be built with Sphinx by running:
cd doc
make htmlThe tutorials are then displayed in the left sidebar of the generated HTML documentation. They can also be accessed directly from the tutorials folder.
The authors of the original implementations integrated to this packages are:
See the references in the documentation for more details.
This project is licensed under the GNU GPL version 3 - see the LICENSE file for details.
© 2024, Max Planck Society / Software Workshop - Max Planck Institute for Intelligent Systems
