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ProbINet - Release Notes for Version 1.0.0 🛠️✨

31 Jan 16:09
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ProbINet - Release Notes for Version 1.0.0 🛠️✨ Latest
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We’re excited to announce the first release of ProbINet, a Python package designed for probabilistic network analysis. This initial version introduces powerful tools and features to help users perform probabilistic network inference, generate synthetic networks, and evaluate models effectively.

🚀 Features

1. Probabilistic Network Inference

The package includes five probabilistic models for network inference, empowering users with flexible and robust tools for analyzing their network data. These models are:

  1. MTCOV: https://www.nature.com/articles/s41598-020-72626-y
  2. CRep: https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.3.023209
  3. JointCRep: https://academic.oup.com/comnet/article/10/4/cnac034/6658441?login=true
  4. DynCRep: https://iopscience.iop.org/article/10.1088/2632-072X/ac52e6
  5. ACD: https://journalofbigdata.springeropen.com/articles/10.1186/s40537-022-00669-1

2. Synthetic Network Generation

Using the package's generative models (synthetic), users can:

  • Fit parameters to real network data.
  • Generate synthetic networks that resemble real-world networks, capturing their essential properties.

3. Model Selection Module

The built-in model_selection module enables:

  • Automated selection of the best model for your data.
  • Easy configuration and tuning of model parameters to improve performance.

4. Performance Evaluation Metrics

Evaluate and compare models effectively with the included performance metrics. These tools provide insight into the strengths and weaknesses of the applied probabilistic methods.

🔍 Downstream Tasks

After fitting a model to your data, ProbINet enables users to perform a variety of downstream tasks, including:

  • Community Detection: Identifying groups within networks.
  • Reciprocity Estimation: Measuring mutual connections within the network.
  • Anomaly Detection: Detecting unusual or unexpected patterns in network data.
  • Link Prediction: Predicting missing or future connections between nodes.

These powerful capabilities make ProbINet a versatile tool for analyzing and working with network data.

📖 Tutorials and Documentation

  • Step-by-step tutorials for every major feature of the package.
  • Real-world examples on how to:
    • Fit models to data.
    • Generate synthetic networks.
    • Select the best parameters and models.
  • Guides to performing downstream tasks like community detection, reciprocity estimation, anomaly detection, and link prediction.

🔧 Use Cases

ProbINet is ideal for researchers, data scientists, and engineers working with network data, such as:

  • Social networks, e.g., interactions between users or groups.
  • Biological interaction networks, e.g., gene or protein interaction networks.
  • Infrastructure/communication networks, e.g., transportation, energy grids, or telecommunications.
  • Synthetic data generation for benchmarking or testing algorithms.

🛠️ Getting Started

Install the package with:

pip install probinet

Learn more by exploring the documentation and tutorials at:
➡️ https://mpi-is.github.io/probinet/

🙌 Community and Feedback

This version is just the beginning, and we are eager to hear your feedback! 🚀
If you encounter any issues or have feature requests, don’t hesitate to reach out or file an issue on our GitHub repository.

Thank you for using ProbINet!

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