This repository contains a Python implementation of the DeepWalk algorithm, including the Skip-gram model. DeepWalk is a popular algorithm for learning latent representations of vertices in a network, which can be used for various downstream tasks such as node classification, link prediction, and community detection.

• Implementation of the DeepWalk algorithm based on the karateclub library by Benedek Rozemberczki
• Skip-gram model for learning node embeddings
• Random walk generator for sampling node sequences from the graph
• Training loop with early stopping mechanism
• Visualization of learned node embeddings using t-SNE dimensionality reduction

• Python 3.x
• NumPy
• NetworkX
• scikit-learn
• Matplotlib

1. Clone the repository:
   git clone https://github.com/your-username/deepwalk-implementation.git

2. Install the required dependencies: pip install numpy networkx scikit-learn matplotlib

3. Run the main.ipynb notebook to train the DeepWalk model on the Zachary's Karate Club dataset and visualize the learned node embeddings.

• activation.py: Contains the softmax activation function.
• deepwalk.py: Implements the DeepWalk algorithm and the random walk generator.
• loss.py: Defines the cross-entropy loss function.
• main.ipynb: Notebook demonstrating the usage of the DeepWalk implementation on the Karate Club dataset.
• skip_gram.py: Implements the Skip-gram model for learning node embeddings.

• The current implementation does not include hierarchical softmax or negative sampling, which are techniques used in the original DeepWalk paper to improve training efficiency.

• Perozzi, B., Al-Rfou, R., & Skiena, S. (2014). DeepWalk: Online Learning of Social Representations. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 701-710).
• karateclub library by Benedek Rozemberczki

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