Introduction

Graph Neural Networks take the concept of Graph data structure and try to model these graphs using machine learning. Most of the real world data can be modelled as a graph. This helps in creating a lot of good structures between the data. Applying machine learning to such graph data can help in giving solutions to a lot of problems like - protein folding, drug discovery, ETA prediction based on traffic.

Table Of Contents

1. Basics of Graph Neural Networks

2. Graph Machine Learning Tasks in Detail

3. Node Embeddings

Papers

References

1. Deepfindr Graph Neural Networks Playlist
2. CS224W Machine Learning With Graphs