logit-graph 0.1.0

Probabilistic logit-based graph model and utilities

Probabilistic Graph Model

Overview

This repository contains an implementation of a probabilistic graph model using logistic regression to predict connections between vertices based on their attributes and interconnections. The model employs a logistic function to estimate the probability of connection between any given pair of vertices in the graph.

Repository Structure

• notebooks/: Contains the notebooks used to generate the results in the paper.
• src/: Contains the source code of the model.
• data/: Contains the datasets used in the experiments.

Key Features

• Logistic regression-based graph modeling
• Vertex attribute and interconnection analysis
• Probability estimation for vertex connections

Installation

To set up the project environment:

1. Clone this repository
2. Install the required dependencies:

   pip install -r requirements.txt
   

Usage

1. Prepare your graph data in the appropriate format (see data/ for examples).
2. Use the scripts in src/ to run the model on your data.
3. Explore the Jupyter notebooks in notebooks/ for detailed analysis and visualization.

Contributing

Contributions to this project are welcome. Please fork the repository and submit a pull request with your changes.

License

This project is licensed under the MIT License - see the LICENSE file for details.