HusseinLezzaik / Graph-Neural-Networks

Code for building GNNs, and Classification/Regression of Nodes/Links/Graphs

Graph Neural Networks - Classification/Regression

Introduction

Graphs express entities (nodes) along with their relations (edges), and both nodes and edges can be typed (e.g., "user" and "item" are two different types of nodes). DGL provides a graph-centric programming abstraction with its core data structure – DGLGraph. DGLGraph provides its interface to handle a graph’s structure, its node/edge features, and the resulting computations that can be performed using these components.

Overview of the Repository

In this repo, you'll find code for:

• Build_GNN_module.py: code for DGL’s message passing APIs, and GraphSAGE convolution module.
• DGL_Graph_Construction.py: code for graph construction from scratch.
• Link_Prediction_Using_GNNs.py: code for link prediction using dgl.
• Make_your_own_dataset.py: code for creating a cataset for node classification or link prediction from csv.
• Node_Classification_with_DGL.py: code for building a GNN for semi-supervised node classification.
• Training_GNN_for_Graph_Classfication.py: code for training gnn model for graph classification.

Getting Started

1. Clone repo: git clone https://github.com/HusseinLezzaik/Graph-Neural-Networks.git
2. Install dependencies:

   conda create -n graph-nets python=3.8
   conda activate graph-nets
   pip install -r requirements.txt
   

3. You also need to download the Deep Graph Library (DGL) from here. It works fine with Ubunut 20.04.

Acknowledgements

Adapted from the official DGL documentation here.

Contact

• Hussein Lezzaik : hussein dot lezzaik at gmail dot com