Request to Add CoreRec: A Graph-Based Recommendation Engine

Dear CoreNet Team,

I am writing to propose the addition of a new recommendation engine, **CoreRec**, to the CoreNet repository/technology. CoreRec is a cutting-edge recommendation engine specifically designed for graph-based algorithms. It seamlessly integrates advanced neural network architectures and excels in node recommendations, model training, and graph visualizations.

**Key Features of CoreRec:**
- **GraphTransformer Model:** A Transformer model tailored for graph data with customizable parameters.
- **GraphDataset:** A PyTorch dataset for efficient handling of graph data.
- **Training Functionality:** Comprehensive training functions for various graph-based machine learning models.
- **Prediction Capability:** Accurate prediction of similar nodes within a graph.
- **Graph Visualization:** Robust 2D and 3D graph visualization tools.

**Benefits of Including CoreRec in CoreNet:**
- **Enhanced Recommendation Capabilities:** Leverage advanced graph algorithms to improve recommendation accuracy.
- **Integration with CoreNet:** Seamlessly integrate CoreRec's functionalities with existing CoreNet infrastructure.
- **Community Collaboration:** Foster collaboration and innovation within the CoreNet community by providing a state-of-the-art recommendation engine.

**Repository URL:** [CoreRec GitHub Repository](https://github.com/vishesh9131/CoreRec)

We believe that CoreRec would be a valuable addition to the CoreNet repository/technology and look forward to your feedback and consideration.

Thank you for your time and attention.
Best regards,

Vishesh Yadav
[mail](vishesh.12322114@lpu.in)
[corerec site](https://vishesh9131.github.io/SIte-CoreRec/)

The proposal names CoreRec and its external repository, but no CoreNet files, tests, or entry points. Start by reviewing the CoreRec repository and CoreNet's existing model-training structure to define an integration plan. Done requires an agreed scope, integration criteria, and tests for the proposed recommendation, training, prediction, and visualization capabilities.

Written by the indexing model from the issue text.