Keras implementation of the graph attention networks (GAT) by Veličković et al. (2017; https://arxiv.org/abs/1710.10903)

Demonstrating and visualising graph data science and ML using neo4j and GDS with the Northwind and Cora datasets

PyTorch Geometric implementations of GraphSAGE and GAT (Graph Attention Networks) for node classification on citation networks.

Graph Machine Learning on the Cora citation dataset using GNN (GCN), traditional ML models, and Genetic Algorithm for hyperparameter optimization.

A project using Graph Neural Networks (GNNs) to classify nodes in the Cora citation network. Implements GCN and GraphSAGE models using PyTorch Geometric to classify academic papers based on citation relationships. Includes preprocessing, model training, evaluation, and visualizations.

Create Cora Dataset CSV files from official data.

Graph Neural Network for Node Classification

Node Classification on large Knowledge Graphs of Cora Dataset using Graph Neural Network (GNN) in Pytorch.