machine learning

There is something about graph algorithms - they are just always fun to think about! With that said, Graph Neural Netorks (GNN) are no exception. These are a form of neural network, but designed in a way to learn things about graphs. They have proven to be usefull for a plethora of graph learning problems in fields such as drug discovery, recommender systems, and protein folding. In this post, I will present the difficulty of directly applying neural networks (NN) to graphs, how message passing NN solves this, and end by looking at the expressive power of such GNN.