In the previous chapter, you used a vector to represent each node in the network. The vectors were handcrafted based on the features you deemed essential. In this chapter, you will learn how to automatically generate node representation vectors using a node embedding model. Node embedding models fall under the dimensionality reduction category.
An example of feature engineering and dimensionality reduction is the body mass index (BMI). BMI is commonly used to define obesity. To precisely characterize obesity, you could look at a person’s height and weight, and measure their fat percentage, muscle content, and waist circumference. In this case, you would be dealing with five input features to predict obesity. Instead of having to measure all five features before an observation can be made, the doctors came up with a BMI.