This repository contains implementations of various graph representation learning algorithms, based on the methods presented in the book Graph Representation Learning. The goal of this project is to provide a foundational understanding of graph-based algorithms and their applications in network analysis, node classification, and link prediction.
🚧 Work in Progress 🚧
- Graph Convolutional Networks (GCNs)
- Graph Neural Networks (GNNs)
- Factorization-based Embeddings
- Random Walk-based Methods
- Deep Sets
- Traditional Graph generation (ER, PA, SBM)
- Skip Connections
- Graph Attention Networks
- Graph spectral clustering
- Simple Graph Convolution
- Graph VAE (node latents & graph latents)
- Graph AE
- Pooling
- Graph GAN