Metadata-Version: 2.1
Name: machine-learning-with-graph
Version: 0.0.2
Summary: A comprehensive package for graph-based machine learning algorithms.
Home-page: https://github.com/yourusername/machine-learning-with-graph
Author: Susheel Gounder and Parikshit Urs
Author-email: susheelg1107@gmail.com
License: MIT
Project-URL: Bug Reports, https://github.com/susheelg1197/machine-learning-with-graph/issues
Project-URL: Source, https://github.com/susheelg1197/machine-learning-with-graph
Keywords: graph neural networks machine learning GNN GCN GAT
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: certifi ==2023.11.17
Requires-Dist: charset-normalizer ==3.3.2
Requires-Dist: colorama ==0.4.6
Requires-Dist: dgl ==1.1.2
Requires-Dist: filelock ==3.13.1
Requires-Dist: fsspec ==2023.12.0
Requires-Dist: gensim ==4.3.2
Requires-Dist: idna ==3.6
Requires-Dist: iniconfig ==2.0.0
Requires-Dist: Jinja2 ==3.1.2
Requires-Dist: joblib ==1.3.2
Requires-Dist: MarkupSafe ==2.1.3
Requires-Dist: mpmath ==1.3.0
Requires-Dist: networkx ==3.2.1
Requires-Dist: numpy ==1.26.2
Requires-Dist: packaging ==23.2
Requires-Dist: pluggy ==1.3.0
Requires-Dist: psutil ==5.9.6
Requires-Dist: pytest ==7.4.3
Requires-Dist: requests ==2.31.0
Requires-Dist: scikit-learn ==1.3.2
Requires-Dist: scipy ==1.11.4
Requires-Dist: smart-open ==6.4.0
Requires-Dist: sympy ==1.12
Requires-Dist: threadpoolctl ==3.2.0
Requires-Dist: torch ==2.1.1
Requires-Dist: tqdm ==4.66.1
Requires-Dist: typing-extensions ==4.8.0
Requires-Dist: urllib3 ==2.1.0

# Machine Learning with Graphs Library

This package provides a comprehensive suite of graph-based machine learning algorithms, encapsulated in an easy-to-use Python library.

## Features

- Graph Neural Networks (GNNs) including GCNs, GATs, and more.
- Graph clustering algorithms such as Spectral Clustering and Louvain method.
- Graph embedding methods like Node2Vec and DeepWalk.
- And many other graph-based algorithms.

## Installation

To install the package, run the following command:

```bash
pip install machine_learning_with_graph
