Graph machine learning

The goal of any ML exercise is to learn a mapping F from an input space X to an output space y. Early machine learning methods required feature engineering to define the appropriate features, whereas DL methods can infer the features from the training data itself. DL works by hypothesizing a model M with random weights , formulating the task as an optimization problem over the parameters :

and using gradient descent to update the model weights over multiple iterations until the parameters converge:

Not surprisingly, GNNs follow this basic model as well.

However, as you have seen in previous chapters, ML and DL are often optimized for specific structures. For example, you might instinctively choose a simple FeedForward Network (FFN) or “dense” network when working with tabular data, a Convolutional Neural Network (CNN) when dealing with image data, and a Recurrent Neural Network (RNN) when dealing with sequence data like text...